{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anomaly Detection in Timeseries: A Comprehensive Evaluation\n",
    "\n",
    "Plots for our paper on TSAD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "import re\n",
    "import json\n",
    "import copy\n",
    "import warnings\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "\n",
    "from IPython.display import display, Markdown, Latex\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.lines import Line2D\n",
    "from matplotlib.markers import MarkerStyle\n",
    "from matplotlib.legend_handler import HandlerBase\n",
    "from matplotlib.patches import Rectangle\n",
    "\n",
    "from itertools import cycle\n",
    "from pathlib import Path\n",
    "\n",
    "from timeeval import DatasetManager as Datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__change fontsize__ (default: 10) __and figure size__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "single_column_figwidth = 10\n",
    "double_column_figwidth = 20\n",
    "plt.rcParams.update({\n",
    "    \"font.size\": 12,\n",
    "    \"font.weight\": \"regular\",  # \"bold\"\n",
    "    \"figure.figsize\": (double_column_figwidth, 8)\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "method_family_colormap = plt.get_cmap(\"Dark2\")\n",
    "learning_type_colormap = plt.get_cmap(\"tab20\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define data and results folder:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Selecting:\n",
      "Data paths:\n",
      "\tGutenTAG: /home/projects/akita/data/test-cases\n",
      "\tBenchmark: /home/projects/akita/data/benchmark-data/data-processed\n",
      "Result path:\n",
      "\tGutenTAG: /home/projects/akita/results/2021-12-03_runtime-gutentag-2-merged\n",
      "\tBenchmark: /home/projects/akita/results/2022-02-21_runtime-benchmark-2-merged\n"
     ]
    }
   ],
   "source": [
    "# constants and configuration\n",
    "gutentag_data_path = Path(\"/home/projects/akita/data\") / \"test-cases\"\n",
    "benchmark_data_path = Path(\"/home/projects/akita/data\") / \"benchmark-data\" / \"data-processed\"\n",
    "result_root_path = Path(\"/home/projects/akita/results\").resolve()\n",
    "\n",
    "baselines = [\"normal\", \"Random\", \"increasing\", \"Normal Baseline\", \"Increasing Baseline\", \"Random Baseline\"]\n",
    "ignore_collections = [\"Keogh\", \"SWaT\", \"WADI\"]\n",
    "\n",
    "# build paths\n",
    "gutentag_data_path = gutentag_data_path.resolve()\n",
    "benchmark_data_path = benchmark_data_path.resolve()\n",
    "gutentag_result_path = result_root_path /  \"2021-12-03_runtime-gutentag-2-merged\"\n",
    "benchmark_result_path = result_root_path / \"2022-02-21_runtime-benchmark-2-merged\"\n",
    "print(\"\\nSelecting:\")\n",
    "print(f\"Data paths:\\n\\tGutenTAG: {gutentag_data_path}\\n\\tBenchmark: {benchmark_data_path}\")\n",
    "print(f\"Result path:\\n\\tGutenTAG: {gutentag_result_path}\\n\\tBenchmark: {benchmark_result_path}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading GutenTAG results from /home/projects/akita/results/2021-12-03_runtime-gutentag-2-merged\n",
      "Reading Benchmark results from /home/projects/akita/results/2022-02-21_runtime-benchmark-2-merged\n"
     ]
    }
   ],
   "source": [
    "def prepare_df(df):\n",
    "    # aggregate runtime\n",
    "    df.insert(len(df.columns), \"overall_time\", df[\"execute_main_time\"].fillna(0) + df[\"train_main_time\"].fillna(0))\n",
    "\n",
    "    # add RANGE_PR_AUC if it is not part of the results\n",
    "    if \"RANGE_PR_AUC\" not in df.columns:\n",
    "        df.insert(len(df.columns), \"RANGE_PR_AUC\", np.nan)\n",
    "\n",
    "    # remove all duplicates (not necessary, but sometimes, we have some)\n",
    "    df.drop_duplicates(inplace=True)\n",
    "    \n",
    "    # remove all baseline algorithms\n",
    "    df.drop(df.index[df[\"algorithm\"].isin(baselines)], errors=\"ignore\", inplace=True)\n",
    "    \n",
    "    # remove additional dataset collections\n",
    "    df.drop(df.index[df[\"collection\"].isin(ignore_collections)], errors=\"ignore\", inplace=True)\n",
    "\n",
    "# load results\n",
    "print(f\"Reading GutenTAG results from {gutentag_result_path.resolve()}\")\n",
    "df_gt = pd.read_csv(gutentag_result_path / \"results.csv\")\n",
    "print(f\"Reading Benchmark results from {benchmark_result_path.resolve()}\")\n",
    "df_bm = pd.read_csv(benchmark_result_path / \"results.csv\")\n",
    "    \n",
    "# fix dataset name for GutenTAG datasets\n",
    "df_gt[\"dataset\"] = df_gt[\"dataset\"].str.split(\".\").str[0]\n",
    "\n",
    "prepare_df(df_gt)\n",
    "prepare_df(df_bm)\n",
    "\n",
    "# only use best hyper parameters for an algorithm-collection-dataset combination (GutenTAG)\n",
    "def filter_groups(group):\n",
    "    if len(group) > 1:\n",
    "        group = group.sort_values(by=\"ROC_AUC\", ascending=False)\n",
    "    return group[:1]\n",
    "\n",
    "df_grouped = df_gt.groupby(by=[\"algorithm\", \"collection\", \"dataset\"])\n",
    "df_grouped = df_grouped.apply(filter_groups)\n",
    "df_gt = df_grouped.reset_index(drop=True)\n",
    "df_gt = df_gt.sort_values(by=[\"algorithm\", \"dataset\"])\n",
    "\n",
    "# combine results\n",
    "df = pd.concat([df_gt, df_bm], ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract and add error categories:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_be83f_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >algorithm</th>\n",
       "      <th class=\"col_heading level0 col0\" >ALL (sum)</th>\n",
       "      <th class=\"col_heading level0 col1\" >ARIMA</th>\n",
       "      <th class=\"col_heading level0 col2\" >Bagel</th>\n",
       "      <th class=\"col_heading level0 col3\" >CBLOF</th>\n",
       "      <th class=\"col_heading level0 col4\" >COF</th>\n",
       "      <th class=\"col_heading level0 col5\" >COPOD</th>\n",
       "      <th class=\"col_heading level0 col6\" >DBStream</th>\n",
       "      <th class=\"col_heading level0 col7\" >DSPOT</th>\n",
       "      <th class=\"col_heading level0 col8\" >DWT-MLEAD</th>\n",
       "      <th class=\"col_heading level0 col9\" >DeepAnT</th>\n",
       "      <th class=\"col_heading level0 col10\" >Donut</th>\n",
       "      <th class=\"col_heading level0 col11\" >EncDec-AD</th>\n",
       "      <th class=\"col_heading level0 col12\" >Extended Isolation Forest (EIF)</th>\n",
       "      <th class=\"col_heading level0 col13\" >FFT</th>\n",
       "      <th class=\"col_heading level0 col14\" >GrammarViz</th>\n",
       "      <th class=\"col_heading level0 col15\" >HBOS</th>\n",
       "      <th class=\"col_heading level0 col16\" >HOT SAX</th>\n",
       "      <th class=\"col_heading level0 col17\" >HealthESN</th>\n",
       "      <th class=\"col_heading level0 col18\" >Hybrid Isolation Forest (HIF)</th>\n",
       "      <th class=\"col_heading level0 col19\" >Hybrid KNN</th>\n",
       "      <th class=\"col_heading level0 col20\" >IF-LOF</th>\n",
       "      <th class=\"col_heading level0 col21\" >ImageEmbeddingCAE</th>\n",
       "      <th class=\"col_heading level0 col22\" >Isolation Forest (iForest)</th>\n",
       "      <th class=\"col_heading level0 col23\" >KNN</th>\n",
       "      <th class=\"col_heading level0 col24\" >LOF</th>\n",
       "      <th class=\"col_heading level0 col25\" >LSTM-AD</th>\n",
       "      <th class=\"col_heading level0 col26\" >LaserDBN</th>\n",
       "      <th class=\"col_heading level0 col27\" >Left STAMPi</th>\n",
       "      <th class=\"col_heading level0 col28\" >MedianMethod</th>\n",
       "      <th class=\"col_heading level0 col29\" >MultiHMM</th>\n",
       "      <th class=\"col_heading level0 col30\" >NormA</th>\n",
       "      <th class=\"col_heading level0 col31\" >Normalizing Flows</th>\n",
       "      <th class=\"col_heading level0 col32\" >NumentaHTM</th>\n",
       "      <th class=\"col_heading level0 col33\" >OceanWNN</th>\n",
       "      <th class=\"col_heading level0 col34\" >OmniAnomaly</th>\n",
       "      <th class=\"col_heading level0 col35\" >PCC</th>\n",
       "      <th class=\"col_heading level0 col36\" >PCI</th>\n",
       "      <th class=\"col_heading level0 col37\" >PST</th>\n",
       "      <th class=\"col_heading level0 col38\" >PhaseSpace-SVM</th>\n",
       "      <th class=\"col_heading level0 col39\" >Random Black Forest (RR)</th>\n",
       "      <th class=\"col_heading level0 col40\" >Random Forest Regressor (RR)</th>\n",
       "      <th class=\"col_heading level0 col41\" >RobustPCA</th>\n",
       "      <th class=\"col_heading level0 col42\" >S-H-ESD (Twitter)</th>\n",
       "      <th class=\"col_heading level0 col43\" >SAND</th>\n",
       "      <th class=\"col_heading level0 col44\" >SR-CNN</th>\n",
       "      <th class=\"col_heading level0 col45\" >SSA</th>\n",
       "      <th class=\"col_heading level0 col46\" >STAMP</th>\n",
       "      <th class=\"col_heading level0 col47\" >STOMP</th>\n",
       "      <th class=\"col_heading level0 col48\" >Series2Graph</th>\n",
       "      <th class=\"col_heading level0 col49\" >Spectral Residual (SR)</th>\n",
       "      <th class=\"col_heading level0 col50\" >Subsequence IF</th>\n",
       "      <th class=\"col_heading level0 col51\" >Subsequence LOF</th>\n",
       "      <th class=\"col_heading level0 col52\" >TARZAN</th>\n",
       "      <th class=\"col_heading level0 col53\" >TAnoGan</th>\n",
       "      <th class=\"col_heading level0 col54\" >TSBitmap</th>\n",
       "      <th class=\"col_heading level0 col55\" >Telemanom</th>\n",
       "      <th class=\"col_heading level0 col56\" >Torsk</th>\n",
       "      <th class=\"col_heading level0 col57\" >Triple ES (Holt-Winter's)</th>\n",
       "      <th class=\"col_heading level0 col58\" >VALMOD</th>\n",
       "      <th class=\"col_heading level0 col59\" >XGBoosting (RR)</th>\n",
       "      <th class=\"col_heading level0 col60\" >k-Means</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >error_category</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "      <th class=\"blank col15\" >&nbsp;</th>\n",
       "      <th class=\"blank col16\" >&nbsp;</th>\n",
       "      <th class=\"blank col17\" >&nbsp;</th>\n",
       "      <th class=\"blank col18\" >&nbsp;</th>\n",
       "      <th class=\"blank col19\" >&nbsp;</th>\n",
       "      <th class=\"blank col20\" >&nbsp;</th>\n",
       "      <th class=\"blank col21\" >&nbsp;</th>\n",
       "      <th class=\"blank col22\" >&nbsp;</th>\n",
       "      <th class=\"blank col23\" >&nbsp;</th>\n",
       "      <th class=\"blank col24\" >&nbsp;</th>\n",
       "      <th class=\"blank col25\" >&nbsp;</th>\n",
       "      <th class=\"blank col26\" >&nbsp;</th>\n",
       "      <th class=\"blank col27\" >&nbsp;</th>\n",
       "      <th class=\"blank col28\" >&nbsp;</th>\n",
       "      <th class=\"blank col29\" >&nbsp;</th>\n",
       "      <th class=\"blank col30\" >&nbsp;</th>\n",
       "      <th class=\"blank col31\" >&nbsp;</th>\n",
       "      <th class=\"blank col32\" >&nbsp;</th>\n",
       "      <th class=\"blank col33\" >&nbsp;</th>\n",
       "      <th class=\"blank col34\" >&nbsp;</th>\n",
       "      <th class=\"blank col35\" >&nbsp;</th>\n",
       "      <th class=\"blank col36\" >&nbsp;</th>\n",
       "      <th class=\"blank col37\" >&nbsp;</th>\n",
       "      <th class=\"blank col38\" >&nbsp;</th>\n",
       "      <th class=\"blank col39\" >&nbsp;</th>\n",
       "      <th class=\"blank col40\" >&nbsp;</th>\n",
       "      <th class=\"blank col41\" >&nbsp;</th>\n",
       "      <th class=\"blank col42\" >&nbsp;</th>\n",
       "      <th class=\"blank col43\" >&nbsp;</th>\n",
       "      <th class=\"blank col44\" >&nbsp;</th>\n",
       "      <th class=\"blank col45\" >&nbsp;</th>\n",
       "      <th class=\"blank col46\" >&nbsp;</th>\n",
       "      <th class=\"blank col47\" >&nbsp;</th>\n",
       "      <th class=\"blank col48\" >&nbsp;</th>\n",
       "      <th class=\"blank col49\" >&nbsp;</th>\n",
       "      <th class=\"blank col50\" >&nbsp;</th>\n",
       "      <th class=\"blank col51\" >&nbsp;</th>\n",
       "      <th class=\"blank col52\" >&nbsp;</th>\n",
       "      <th class=\"blank col53\" >&nbsp;</th>\n",
       "      <th class=\"blank col54\" >&nbsp;</th>\n",
       "      <th class=\"blank col55\" >&nbsp;</th>\n",
       "      <th class=\"blank col56\" >&nbsp;</th>\n",
       "      <th class=\"blank col57\" >&nbsp;</th>\n",
       "      <th class=\"blank col58\" >&nbsp;</th>\n",
       "      <th class=\"blank col59\" >&nbsp;</th>\n",
       "      <th class=\"blank col60\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row0\" class=\"row_heading level0 row0\" >- OK -</th>\n",
       "      <td id=\"T_be83f_row0_col0\" class=\"data row0 col0\" >41457</td>\n",
       "      <td id=\"T_be83f_row0_col1\" class=\"data row0 col1\" >849</td>\n",
       "      <td id=\"T_be83f_row0_col2\" class=\"data row0 col2\" >373</td>\n",
       "      <td id=\"T_be83f_row0_col3\" class=\"data row0 col3\" >1100</td>\n",
       "      <td id=\"T_be83f_row0_col4\" class=\"data row0 col4\" >748</td>\n",
       "      <td id=\"T_be83f_row0_col5\" class=\"data row0 col5\" >1103</td>\n",
       "      <td id=\"T_be83f_row0_col6\" class=\"data row0 col6\" >239</td>\n",
       "      <td id=\"T_be83f_row0_col7\" class=\"data row0 col7\" >857</td>\n",
       "      <td id=\"T_be83f_row0_col8\" class=\"data row0 col8\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col9\" class=\"data row0 col9\" >496</td>\n",
       "      <td id=\"T_be83f_row0_col10\" class=\"data row0 col10\" >452</td>\n",
       "      <td id=\"T_be83f_row0_col11\" class=\"data row0 col11\" >83</td>\n",
       "      <td id=\"T_be83f_row0_col12\" class=\"data row0 col12\" >1123</td>\n",
       "      <td id=\"T_be83f_row0_col13\" class=\"data row0 col13\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col14\" class=\"data row0 col14\" >886</td>\n",
       "      <td id=\"T_be83f_row0_col15\" class=\"data row0 col15\" >1103</td>\n",
       "      <td id=\"T_be83f_row0_col16\" class=\"data row0 col16\" >678</td>\n",
       "      <td id=\"T_be83f_row0_col17\" class=\"data row0 col17\" >178</td>\n",
       "      <td id=\"T_be83f_row0_col18\" class=\"data row0 col18\" >198</td>\n",
       "      <td id=\"T_be83f_row0_col19\" class=\"data row0 col19\" >509</td>\n",
       "      <td id=\"T_be83f_row0_col20\" class=\"data row0 col20\" >1070</td>\n",
       "      <td id=\"T_be83f_row0_col21\" class=\"data row0 col21\" >463</td>\n",
       "      <td id=\"T_be83f_row0_col22\" class=\"data row0 col22\" >1103</td>\n",
       "      <td id=\"T_be83f_row0_col23\" class=\"data row0 col23\" >1103</td>\n",
       "      <td id=\"T_be83f_row0_col24\" class=\"data row0 col24\" >1102</td>\n",
       "      <td id=\"T_be83f_row0_col25\" class=\"data row0 col25\" >166</td>\n",
       "      <td id=\"T_be83f_row0_col26\" class=\"data row0 col26\" >453</td>\n",
       "      <td id=\"T_be83f_row0_col27\" class=\"data row0 col27\" >884</td>\n",
       "      <td id=\"T_be83f_row0_col28\" class=\"data row0 col28\" >912</td>\n",
       "      <td id=\"T_be83f_row0_col29\" class=\"data row0 col29\" >96</td>\n",
       "      <td id=\"T_be83f_row0_col30\" class=\"data row0 col30\" >782</td>\n",
       "      <td id=\"T_be83f_row0_col31\" class=\"data row0 col31\" >67</td>\n",
       "      <td id=\"T_be83f_row0_col32\" class=\"data row0 col32\" >908</td>\n",
       "      <td id=\"T_be83f_row0_col33\" class=\"data row0 col33\" >423</td>\n",
       "      <td id=\"T_be83f_row0_col34\" class=\"data row0 col34\" >538</td>\n",
       "      <td id=\"T_be83f_row0_col35\" class=\"data row0 col35\" >1103</td>\n",
       "      <td id=\"T_be83f_row0_col36\" class=\"data row0 col36\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col37\" class=\"data row0 col37\" >877</td>\n",
       "      <td id=\"T_be83f_row0_col38\" class=\"data row0 col38\" >798</td>\n",
       "      <td id=\"T_be83f_row0_col39\" class=\"data row0 col39\" >460</td>\n",
       "      <td id=\"T_be83f_row0_col40\" class=\"data row0 col40\" >415</td>\n",
       "      <td id=\"T_be83f_row0_col41\" class=\"data row0 col41\" >521</td>\n",
       "      <td id=\"T_be83f_row0_col42\" class=\"data row0 col42\" >468</td>\n",
       "      <td id=\"T_be83f_row0_col43\" class=\"data row0 col43\" >647</td>\n",
       "      <td id=\"T_be83f_row0_col44\" class=\"data row0 col44\" >359</td>\n",
       "      <td id=\"T_be83f_row0_col45\" class=\"data row0 col45\" >908</td>\n",
       "      <td id=\"T_be83f_row0_col46\" class=\"data row0 col46\" >874</td>\n",
       "      <td id=\"T_be83f_row0_col47\" class=\"data row0 col47\" >898</td>\n",
       "      <td id=\"T_be83f_row0_col48\" class=\"data row0 col48\" >863</td>\n",
       "      <td id=\"T_be83f_row0_col49\" class=\"data row0 col49\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col50\" class=\"data row0 col50\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col51\" class=\"data row0 col51\" >895</td>\n",
       "      <td id=\"T_be83f_row0_col52\" class=\"data row0 col52\" >386</td>\n",
       "      <td id=\"T_be83f_row0_col53\" class=\"data row0 col53\" >176</td>\n",
       "      <td id=\"T_be83f_row0_col54\" class=\"data row0 col54\" >913</td>\n",
       "      <td id=\"T_be83f_row0_col55\" class=\"data row0 col55\" >518</td>\n",
       "      <td id=\"T_be83f_row0_col56\" class=\"data row0 col56\" >931</td>\n",
       "      <td id=\"T_be83f_row0_col57\" class=\"data row0 col57\" >696</td>\n",
       "      <td id=\"T_be83f_row0_col58\" class=\"data row0 col58\" >722</td>\n",
       "      <td id=\"T_be83f_row0_col59\" class=\"data row0 col59\" >466</td>\n",
       "      <td id=\"T_be83f_row0_col60\" class=\"data row0 col60\" >953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row1\" class=\"row_heading level0 row1\" >- OOM -</th>\n",
       "      <td id=\"T_be83f_row1_col0\" class=\"data row1 col0\" >1413</td>\n",
       "      <td id=\"T_be83f_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_be83f_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_be83f_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_be83f_row1_col4\" class=\"data row1 col4\" >355</td>\n",
       "      <td id=\"T_be83f_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_be83f_row1_col6\" class=\"data row1 col6\" >77</td>\n",
       "      <td id=\"T_be83f_row1_col7\" class=\"data row1 col7\" ></td>\n",
       "      <td id=\"T_be83f_row1_col8\" class=\"data row1 col8\" ></td>\n",
       "      <td id=\"T_be83f_row1_col9\" class=\"data row1 col9\" ></td>\n",
       "      <td id=\"T_be83f_row1_col10\" class=\"data row1 col10\" >3</td>\n",
       "      <td id=\"T_be83f_row1_col11\" class=\"data row1 col11\" >133</td>\n",
       "      <td id=\"T_be83f_row1_col12\" class=\"data row1 col12\" ></td>\n",
       "      <td id=\"T_be83f_row1_col13\" class=\"data row1 col13\" ></td>\n",
       "      <td id=\"T_be83f_row1_col14\" class=\"data row1 col14\" ></td>\n",
       "      <td id=\"T_be83f_row1_col15\" class=\"data row1 col15\" ></td>\n",
       "      <td id=\"T_be83f_row1_col16\" class=\"data row1 col16\" >10</td>\n",
       "      <td id=\"T_be83f_row1_col17\" class=\"data row1 col17\" >7</td>\n",
       "      <td id=\"T_be83f_row1_col18\" class=\"data row1 col18\" ></td>\n",
       "      <td id=\"T_be83f_row1_col19\" class=\"data row1 col19\" >6</td>\n",
       "      <td id=\"T_be83f_row1_col20\" class=\"data row1 col20\" ></td>\n",
       "      <td id=\"T_be83f_row1_col21\" class=\"data row1 col21\" ></td>\n",
       "      <td id=\"T_be83f_row1_col22\" class=\"data row1 col22\" ></td>\n",
       "      <td id=\"T_be83f_row1_col23\" class=\"data row1 col23\" ></td>\n",
       "      <td id=\"T_be83f_row1_col24\" class=\"data row1 col24\" ></td>\n",
       "      <td id=\"T_be83f_row1_col25\" class=\"data row1 col25\" >312</td>\n",
       "      <td id=\"T_be83f_row1_col26\" class=\"data row1 col26\" >1</td>\n",
       "      <td id=\"T_be83f_row1_col27\" class=\"data row1 col27\" ></td>\n",
       "      <td id=\"T_be83f_row1_col28\" class=\"data row1 col28\" ></td>\n",
       "      <td id=\"T_be83f_row1_col29\" class=\"data row1 col29\" ></td>\n",
       "      <td id=\"T_be83f_row1_col30\" class=\"data row1 col30\" >13</td>\n",
       "      <td id=\"T_be83f_row1_col31\" class=\"data row1 col31\" >12</td>\n",
       "      <td id=\"T_be83f_row1_col32\" class=\"data row1 col32\" ></td>\n",
       "      <td id=\"T_be83f_row1_col33\" class=\"data row1 col33\" ></td>\n",
       "      <td id=\"T_be83f_row1_col34\" class=\"data row1 col34\" >20</td>\n",
       "      <td id=\"T_be83f_row1_col35\" class=\"data row1 col35\" ></td>\n",
       "      <td id=\"T_be83f_row1_col36\" class=\"data row1 col36\" ></td>\n",
       "      <td id=\"T_be83f_row1_col37\" class=\"data row1 col37\" >35</td>\n",
       "      <td id=\"T_be83f_row1_col38\" class=\"data row1 col38\" ></td>\n",
       "      <td id=\"T_be83f_row1_col39\" class=\"data row1 col39\" >75</td>\n",
       "      <td id=\"T_be83f_row1_col40\" class=\"data row1 col40\" ></td>\n",
       "      <td id=\"T_be83f_row1_col41\" class=\"data row1 col41\" ></td>\n",
       "      <td id=\"T_be83f_row1_col42\" class=\"data row1 col42\" ></td>\n",
       "      <td id=\"T_be83f_row1_col43\" class=\"data row1 col43\" >12</td>\n",
       "      <td id=\"T_be83f_row1_col44\" class=\"data row1 col44\" ></td>\n",
       "      <td id=\"T_be83f_row1_col45\" class=\"data row1 col45\" ></td>\n",
       "      <td id=\"T_be83f_row1_col46\" class=\"data row1 col46\" ></td>\n",
       "      <td id=\"T_be83f_row1_col47\" class=\"data row1 col47\" ></td>\n",
       "      <td id=\"T_be83f_row1_col48\" class=\"data row1 col48\" ></td>\n",
       "      <td id=\"T_be83f_row1_col49\" class=\"data row1 col49\" ></td>\n",
       "      <td id=\"T_be83f_row1_col50\" class=\"data row1 col50\" ></td>\n",
       "      <td id=\"T_be83f_row1_col51\" class=\"data row1 col51\" ></td>\n",
       "      <td id=\"T_be83f_row1_col52\" class=\"data row1 col52\" ></td>\n",
       "      <td id=\"T_be83f_row1_col53\" class=\"data row1 col53\" >48</td>\n",
       "      <td id=\"T_be83f_row1_col54\" class=\"data row1 col54\" ></td>\n",
       "      <td id=\"T_be83f_row1_col55\" class=\"data row1 col55\" >50</td>\n",
       "      <td id=\"T_be83f_row1_col56\" class=\"data row1 col56\" ></td>\n",
       "      <td id=\"T_be83f_row1_col57\" class=\"data row1 col57\" ></td>\n",
       "      <td id=\"T_be83f_row1_col58\" class=\"data row1 col58\" >80</td>\n",
       "      <td id=\"T_be83f_row1_col59\" class=\"data row1 col59\" >3</td>\n",
       "      <td id=\"T_be83f_row1_col60\" class=\"data row1 col60\" >161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row2\" class=\"row_heading level0 row2\" >- TIMEOUT -</th>\n",
       "      <td id=\"T_be83f_row2_col0\" class=\"data row2 col0\" >2792</td>\n",
       "      <td id=\"T_be83f_row2_col1\" class=\"data row2 col1\" >60</td>\n",
       "      <td id=\"T_be83f_row2_col2\" class=\"data row2 col2\" >87</td>\n",
       "      <td id=\"T_be83f_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_be83f_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_be83f_row2_col5\" class=\"data row2 col5\" ></td>\n",
       "      <td id=\"T_be83f_row2_col6\" class=\"data row2 col6\" >1</td>\n",
       "      <td id=\"T_be83f_row2_col7\" class=\"data row2 col7\" >56</td>\n",
       "      <td id=\"T_be83f_row2_col8\" class=\"data row2 col8\" ></td>\n",
       "      <td id=\"T_be83f_row2_col9\" class=\"data row2 col9\" >48</td>\n",
       "      <td id=\"T_be83f_row2_col10\" class=\"data row2 col10\" >4</td>\n",
       "      <td id=\"T_be83f_row2_col11\" class=\"data row2 col11\" >336</td>\n",
       "      <td id=\"T_be83f_row2_col12\" class=\"data row2 col12\" ></td>\n",
       "      <td id=\"T_be83f_row2_col13\" class=\"data row2 col13\" ></td>\n",
       "      <td id=\"T_be83f_row2_col14\" class=\"data row2 col14\" >27</td>\n",
       "      <td id=\"T_be83f_row2_col15\" class=\"data row2 col15\" ></td>\n",
       "      <td id=\"T_be83f_row2_col16\" class=\"data row2 col16\" >216</td>\n",
       "      <td id=\"T_be83f_row2_col17\" class=\"data row2 col17\" >384</td>\n",
       "      <td id=\"T_be83f_row2_col18\" class=\"data row2 col18\" >1</td>\n",
       "      <td id=\"T_be83f_row2_col19\" class=\"data row2 col19\" >48</td>\n",
       "      <td id=\"T_be83f_row2_col20\" class=\"data row2 col20\" >6</td>\n",
       "      <td id=\"T_be83f_row2_col21\" class=\"data row2 col21\" ></td>\n",
       "      <td id=\"T_be83f_row2_col22\" class=\"data row2 col22\" ></td>\n",
       "      <td id=\"T_be83f_row2_col23\" class=\"data row2 col23\" ></td>\n",
       "      <td id=\"T_be83f_row2_col24\" class=\"data row2 col24\" >1</td>\n",
       "      <td id=\"T_be83f_row2_col25\" class=\"data row2 col25\" >75</td>\n",
       "      <td id=\"T_be83f_row2_col26\" class=\"data row2 col26\" >74</td>\n",
       "      <td id=\"T_be83f_row2_col27\" class=\"data row2 col27\" >16</td>\n",
       "      <td id=\"T_be83f_row2_col28\" class=\"data row2 col28\" ></td>\n",
       "      <td id=\"T_be83f_row2_col29\" class=\"data row2 col29\" ></td>\n",
       "      <td id=\"T_be83f_row2_col30\" class=\"data row2 col30\" >92</td>\n",
       "      <td id=\"T_be83f_row2_col31\" class=\"data row2 col31\" >120</td>\n",
       "      <td id=\"T_be83f_row2_col32\" class=\"data row2 col32\" >4</td>\n",
       "      <td id=\"T_be83f_row2_col33\" class=\"data row2 col33\" ></td>\n",
       "      <td id=\"T_be83f_row2_col34\" class=\"data row2 col34\" ></td>\n",
       "      <td id=\"T_be83f_row2_col35\" class=\"data row2 col35\" ></td>\n",
       "      <td id=\"T_be83f_row2_col36\" class=\"data row2 col36\" ></td>\n",
       "      <td id=\"T_be83f_row2_col37\" class=\"data row2 col37\" ></td>\n",
       "      <td id=\"T_be83f_row2_col38\" class=\"data row2 col38\" >112</td>\n",
       "      <td id=\"T_be83f_row2_col39\" class=\"data row2 col39\" >34</td>\n",
       "      <td id=\"T_be83f_row2_col40\" class=\"data row2 col40\" >54</td>\n",
       "      <td id=\"T_be83f_row2_col41\" class=\"data row2 col41\" >48</td>\n",
       "      <td id=\"T_be83f_row2_col42\" class=\"data row2 col42\" ></td>\n",
       "      <td id=\"T_be83f_row2_col43\" class=\"data row2 col43\" >48</td>\n",
       "      <td id=\"T_be83f_row2_col44\" class=\"data row2 col44\" >103</td>\n",
       "      <td id=\"T_be83f_row2_col45\" class=\"data row2 col45\" >5</td>\n",
       "      <td id=\"T_be83f_row2_col46\" class=\"data row2 col46\" >39</td>\n",
       "      <td id=\"T_be83f_row2_col47\" class=\"data row2 col47\" >15</td>\n",
       "      <td id=\"T_be83f_row2_col48\" class=\"data row2 col48\" ></td>\n",
       "      <td id=\"T_be83f_row2_col49\" class=\"data row2 col49\" ></td>\n",
       "      <td id=\"T_be83f_row2_col50\" class=\"data row2 col50\" ></td>\n",
       "      <td id=\"T_be83f_row2_col51\" class=\"data row2 col51\" >18</td>\n",
       "      <td id=\"T_be83f_row2_col52\" class=\"data row2 col52\" ></td>\n",
       "      <td id=\"T_be83f_row2_col53\" class=\"data row2 col53\" >341</td>\n",
       "      <td id=\"T_be83f_row2_col54\" class=\"data row2 col54\" ></td>\n",
       "      <td id=\"T_be83f_row2_col55\" class=\"data row2 col55\" >1</td>\n",
       "      <td id=\"T_be83f_row2_col56\" class=\"data row2 col56\" >170</td>\n",
       "      <td id=\"T_be83f_row2_col57\" class=\"data row2 col57\" >136</td>\n",
       "      <td id=\"T_be83f_row2_col58\" class=\"data row2 col58\" >12</td>\n",
       "      <td id=\"T_be83f_row2_col59\" class=\"data row2 col59\" ></td>\n",
       "      <td id=\"T_be83f_row2_col60\" class=\"data row2 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row3\" class=\"row_heading level0 row3\" >Bug</th>\n",
       "      <td id=\"T_be83f_row3_col0\" class=\"data row3 col0\" >1068</td>\n",
       "      <td id=\"T_be83f_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_be83f_row3_col2\" class=\"data row3 col2\" >9</td>\n",
       "      <td id=\"T_be83f_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_be83f_row3_col4\" class=\"data row3 col4\" ></td>\n",
       "      <td id=\"T_be83f_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_be83f_row3_col6\" class=\"data row3 col6\" >504</td>\n",
       "      <td id=\"T_be83f_row3_col7\" class=\"data row3 col7\" ></td>\n",
       "      <td id=\"T_be83f_row3_col8\" class=\"data row3 col8\" ></td>\n",
       "      <td id=\"T_be83f_row3_col9\" class=\"data row3 col9\" >22</td>\n",
       "      <td id=\"T_be83f_row3_col10\" class=\"data row3 col10\" >10</td>\n",
       "      <td id=\"T_be83f_row3_col11\" class=\"data row3 col11\" >17</td>\n",
       "      <td id=\"T_be83f_row3_col12\" class=\"data row3 col12\" ></td>\n",
       "      <td id=\"T_be83f_row3_col13\" class=\"data row3 col13\" ></td>\n",
       "      <td id=\"T_be83f_row3_col14\" class=\"data row3 col14\" ></td>\n",
       "      <td id=\"T_be83f_row3_col15\" class=\"data row3 col15\" ></td>\n",
       "      <td id=\"T_be83f_row3_col16\" class=\"data row3 col16\" >9</td>\n",
       "      <td id=\"T_be83f_row3_col17\" class=\"data row3 col17\" ></td>\n",
       "      <td id=\"T_be83f_row3_col18\" class=\"data row3 col18\" ></td>\n",
       "      <td id=\"T_be83f_row3_col19\" class=\"data row3 col19\" >6</td>\n",
       "      <td id=\"T_be83f_row3_col20\" class=\"data row3 col20\" >1</td>\n",
       "      <td id=\"T_be83f_row3_col21\" class=\"data row3 col21\" >6</td>\n",
       "      <td id=\"T_be83f_row3_col22\" class=\"data row3 col22\" ></td>\n",
       "      <td id=\"T_be83f_row3_col23\" class=\"data row3 col23\" ></td>\n",
       "      <td id=\"T_be83f_row3_col24\" class=\"data row3 col24\" ></td>\n",
       "      <td id=\"T_be83f_row3_col25\" class=\"data row3 col25\" >16</td>\n",
       "      <td id=\"T_be83f_row3_col26\" class=\"data row3 col26\" >41</td>\n",
       "      <td id=\"T_be83f_row3_col27\" class=\"data row3 col27\" ></td>\n",
       "      <td id=\"T_be83f_row3_col28\" class=\"data row3 col28\" ></td>\n",
       "      <td id=\"T_be83f_row3_col29\" class=\"data row3 col29\" ></td>\n",
       "      <td id=\"T_be83f_row3_col30\" class=\"data row3 col30\" >9</td>\n",
       "      <td id=\"T_be83f_row3_col31\" class=\"data row3 col31\" ></td>\n",
       "      <td id=\"T_be83f_row3_col32\" class=\"data row3 col32\" >1</td>\n",
       "      <td id=\"T_be83f_row3_col33\" class=\"data row3 col33\" >43</td>\n",
       "      <td id=\"T_be83f_row3_col34\" class=\"data row3 col34\" ></td>\n",
       "      <td id=\"T_be83f_row3_col35\" class=\"data row3 col35\" ></td>\n",
       "      <td id=\"T_be83f_row3_col36\" class=\"data row3 col36\" ></td>\n",
       "      <td id=\"T_be83f_row3_col37\" class=\"data row3 col37\" >1</td>\n",
       "      <td id=\"T_be83f_row3_col38\" class=\"data row3 col38\" ></td>\n",
       "      <td id=\"T_be83f_row3_col39\" class=\"data row3 col39\" ></td>\n",
       "      <td id=\"T_be83f_row3_col40\" class=\"data row3 col40\" ></td>\n",
       "      <td id=\"T_be83f_row3_col41\" class=\"data row3 col41\" ></td>\n",
       "      <td id=\"T_be83f_row3_col42\" class=\"data row3 col42\" ></td>\n",
       "      <td id=\"T_be83f_row3_col43\" class=\"data row3 col43\" >195</td>\n",
       "      <td id=\"T_be83f_row3_col44\" class=\"data row3 col44\" ></td>\n",
       "      <td id=\"T_be83f_row3_col45\" class=\"data row3 col45\" ></td>\n",
       "      <td id=\"T_be83f_row3_col46\" class=\"data row3 col46\" ></td>\n",
       "      <td id=\"T_be83f_row3_col47\" class=\"data row3 col47\" ></td>\n",
       "      <td id=\"T_be83f_row3_col48\" class=\"data row3 col48\" >36</td>\n",
       "      <td id=\"T_be83f_row3_col49\" class=\"data row3 col49\" ></td>\n",
       "      <td id=\"T_be83f_row3_col50\" class=\"data row3 col50\" ></td>\n",
       "      <td id=\"T_be83f_row3_col51\" class=\"data row3 col51\" ></td>\n",
       "      <td id=\"T_be83f_row3_col52\" class=\"data row3 col52\" >32</td>\n",
       "      <td id=\"T_be83f_row3_col53\" class=\"data row3 col53\" ></td>\n",
       "      <td id=\"T_be83f_row3_col54\" class=\"data row3 col54\" ></td>\n",
       "      <td id=\"T_be83f_row3_col55\" class=\"data row3 col55\" ></td>\n",
       "      <td id=\"T_be83f_row3_col56\" class=\"data row3 col56\" >2</td>\n",
       "      <td id=\"T_be83f_row3_col57\" class=\"data row3 col57\" ></td>\n",
       "      <td id=\"T_be83f_row3_col58\" class=\"data row3 col58\" >99</td>\n",
       "      <td id=\"T_be83f_row3_col59\" class=\"data row3 col59\" ></td>\n",
       "      <td id=\"T_be83f_row3_col60\" class=\"data row3 col60\" >9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row4\" class=\"row_heading level0 row4\" >Incompatible parameters</th>\n",
       "      <td id=\"T_be83f_row4_col0\" class=\"data row4 col0\" >725</td>\n",
       "      <td id=\"T_be83f_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_be83f_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_be83f_row4_col3\" class=\"data row4 col3\" ></td>\n",
       "      <td id=\"T_be83f_row4_col4\" class=\"data row4 col4\" ></td>\n",
       "      <td id=\"T_be83f_row4_col5\" class=\"data row4 col5\" ></td>\n",
       "      <td id=\"T_be83f_row4_col6\" class=\"data row4 col6\" >289</td>\n",
       "      <td id=\"T_be83f_row4_col7\" class=\"data row4 col7\" ></td>\n",
       "      <td id=\"T_be83f_row4_col8\" class=\"data row4 col8\" ></td>\n",
       "      <td id=\"T_be83f_row4_col9\" class=\"data row4 col9\" ></td>\n",
       "      <td id=\"T_be83f_row4_col10\" class=\"data row4 col10\" ></td>\n",
       "      <td id=\"T_be83f_row4_col11\" class=\"data row4 col11\" ></td>\n",
       "      <td id=\"T_be83f_row4_col12\" class=\"data row4 col12\" ></td>\n",
       "      <td id=\"T_be83f_row4_col13\" class=\"data row4 col13\" ></td>\n",
       "      <td id=\"T_be83f_row4_col14\" class=\"data row4 col14\" ></td>\n",
       "      <td id=\"T_be83f_row4_col15\" class=\"data row4 col15\" ></td>\n",
       "      <td id=\"T_be83f_row4_col16\" class=\"data row4 col16\" ></td>\n",
       "      <td id=\"T_be83f_row4_col17\" class=\"data row4 col17\" ></td>\n",
       "      <td id=\"T_be83f_row4_col18\" class=\"data row4 col18\" ></td>\n",
       "      <td id=\"T_be83f_row4_col19\" class=\"data row4 col19\" ></td>\n",
       "      <td id=\"T_be83f_row4_col20\" class=\"data row4 col20\" ></td>\n",
       "      <td id=\"T_be83f_row4_col21\" class=\"data row4 col21\" ></td>\n",
       "      <td id=\"T_be83f_row4_col22\" class=\"data row4 col22\" ></td>\n",
       "      <td id=\"T_be83f_row4_col23\" class=\"data row4 col23\" ></td>\n",
       "      <td id=\"T_be83f_row4_col24\" class=\"data row4 col24\" ></td>\n",
       "      <td id=\"T_be83f_row4_col25\" class=\"data row4 col25\" ></td>\n",
       "      <td id=\"T_be83f_row4_col26\" class=\"data row4 col26\" ></td>\n",
       "      <td id=\"T_be83f_row4_col27\" class=\"data row4 col27\" >2</td>\n",
       "      <td id=\"T_be83f_row4_col28\" class=\"data row4 col28\" ></td>\n",
       "      <td id=\"T_be83f_row4_col29\" class=\"data row4 col29\" ></td>\n",
       "      <td id=\"T_be83f_row4_col30\" class=\"data row4 col30\" ></td>\n",
       "      <td id=\"T_be83f_row4_col31\" class=\"data row4 col31\" ></td>\n",
       "      <td id=\"T_be83f_row4_col32\" class=\"data row4 col32\" ></td>\n",
       "      <td id=\"T_be83f_row4_col33\" class=\"data row4 col33\" ></td>\n",
       "      <td id=\"T_be83f_row4_col34\" class=\"data row4 col34\" ></td>\n",
       "      <td id=\"T_be83f_row4_col35\" class=\"data row4 col35\" ></td>\n",
       "      <td id=\"T_be83f_row4_col36\" class=\"data row4 col36\" ></td>\n",
       "      <td id=\"T_be83f_row4_col37\" class=\"data row4 col37\" ></td>\n",
       "      <td id=\"T_be83f_row4_col38\" class=\"data row4 col38\" ></td>\n",
       "      <td id=\"T_be83f_row4_col39\" class=\"data row4 col39\" ></td>\n",
       "      <td id=\"T_be83f_row4_col40\" class=\"data row4 col40\" ></td>\n",
       "      <td id=\"T_be83f_row4_col41\" class=\"data row4 col41\" ></td>\n",
       "      <td id=\"T_be83f_row4_col42\" class=\"data row4 col42\" >434</td>\n",
       "      <td id=\"T_be83f_row4_col43\" class=\"data row4 col43\" ></td>\n",
       "      <td id=\"T_be83f_row4_col44\" class=\"data row4 col44\" ></td>\n",
       "      <td id=\"T_be83f_row4_col45\" class=\"data row4 col45\" ></td>\n",
       "      <td id=\"T_be83f_row4_col46\" class=\"data row4 col46\" ></td>\n",
       "      <td id=\"T_be83f_row4_col47\" class=\"data row4 col47\" ></td>\n",
       "      <td id=\"T_be83f_row4_col48\" class=\"data row4 col48\" ></td>\n",
       "      <td id=\"T_be83f_row4_col49\" class=\"data row4 col49\" ></td>\n",
       "      <td id=\"T_be83f_row4_col50\" class=\"data row4 col50\" ></td>\n",
       "      <td id=\"T_be83f_row4_col51\" class=\"data row4 col51\" ></td>\n",
       "      <td id=\"T_be83f_row4_col52\" class=\"data row4 col52\" ></td>\n",
       "      <td id=\"T_be83f_row4_col53\" class=\"data row4 col53\" ></td>\n",
       "      <td id=\"T_be83f_row4_col54\" class=\"data row4 col54\" ></td>\n",
       "      <td id=\"T_be83f_row4_col55\" class=\"data row4 col55\" ></td>\n",
       "      <td id=\"T_be83f_row4_col56\" class=\"data row4 col56\" ></td>\n",
       "      <td id=\"T_be83f_row4_col57\" class=\"data row4 col57\" ></td>\n",
       "      <td id=\"T_be83f_row4_col58\" class=\"data row4 col58\" ></td>\n",
       "      <td id=\"T_be83f_row4_col59\" class=\"data row4 col59\" ></td>\n",
       "      <td id=\"T_be83f_row4_col60\" class=\"data row4 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row5\" class=\"row_heading level0 row5\" >Invariance/assumption not met</th>\n",
       "      <td id=\"T_be83f_row5_col0\" class=\"data row5 col0\" >126</td>\n",
       "      <td id=\"T_be83f_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_be83f_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_be83f_row5_col3\" class=\"data row5 col3\" ></td>\n",
       "      <td id=\"T_be83f_row5_col4\" class=\"data row5 col4\" ></td>\n",
       "      <td id=\"T_be83f_row5_col5\" class=\"data row5 col5\" ></td>\n",
       "      <td id=\"T_be83f_row5_col6\" class=\"data row5 col6\" ></td>\n",
       "      <td id=\"T_be83f_row5_col7\" class=\"data row5 col7\" ></td>\n",
       "      <td id=\"T_be83f_row5_col8\" class=\"data row5 col8\" ></td>\n",
       "      <td id=\"T_be83f_row5_col9\" class=\"data row5 col9\" ></td>\n",
       "      <td id=\"T_be83f_row5_col10\" class=\"data row5 col10\" ></td>\n",
       "      <td id=\"T_be83f_row5_col11\" class=\"data row5 col11\" ></td>\n",
       "      <td id=\"T_be83f_row5_col12\" class=\"data row5 col12\" ></td>\n",
       "      <td id=\"T_be83f_row5_col13\" class=\"data row5 col13\" ></td>\n",
       "      <td id=\"T_be83f_row5_col14\" class=\"data row5 col14\" ></td>\n",
       "      <td id=\"T_be83f_row5_col15\" class=\"data row5 col15\" ></td>\n",
       "      <td id=\"T_be83f_row5_col16\" class=\"data row5 col16\" ></td>\n",
       "      <td id=\"T_be83f_row5_col17\" class=\"data row5 col17\" ></td>\n",
       "      <td id=\"T_be83f_row5_col18\" class=\"data row5 col18\" ></td>\n",
       "      <td id=\"T_be83f_row5_col19\" class=\"data row5 col19\" ></td>\n",
       "      <td id=\"T_be83f_row5_col20\" class=\"data row5 col20\" ></td>\n",
       "      <td id=\"T_be83f_row5_col21\" class=\"data row5 col21\" ></td>\n",
       "      <td id=\"T_be83f_row5_col22\" class=\"data row5 col22\" ></td>\n",
       "      <td id=\"T_be83f_row5_col23\" class=\"data row5 col23\" ></td>\n",
       "      <td id=\"T_be83f_row5_col24\" class=\"data row5 col24\" ></td>\n",
       "      <td id=\"T_be83f_row5_col25\" class=\"data row5 col25\" ></td>\n",
       "      <td id=\"T_be83f_row5_col26\" class=\"data row5 col26\" ></td>\n",
       "      <td id=\"T_be83f_row5_col27\" class=\"data row5 col27\" >11</td>\n",
       "      <td id=\"T_be83f_row5_col28\" class=\"data row5 col28\" ></td>\n",
       "      <td id=\"T_be83f_row5_col29\" class=\"data row5 col29\" ></td>\n",
       "      <td id=\"T_be83f_row5_col30\" class=\"data row5 col30\" ></td>\n",
       "      <td id=\"T_be83f_row5_col31\" class=\"data row5 col31\" ></td>\n",
       "      <td id=\"T_be83f_row5_col32\" class=\"data row5 col32\" ></td>\n",
       "      <td id=\"T_be83f_row5_col33\" class=\"data row5 col33\" ></td>\n",
       "      <td id=\"T_be83f_row5_col34\" class=\"data row5 col34\" ></td>\n",
       "      <td id=\"T_be83f_row5_col35\" class=\"data row5 col35\" ></td>\n",
       "      <td id=\"T_be83f_row5_col36\" class=\"data row5 col36\" ></td>\n",
       "      <td id=\"T_be83f_row5_col37\" class=\"data row5 col37\" ></td>\n",
       "      <td id=\"T_be83f_row5_col38\" class=\"data row5 col38\" ></td>\n",
       "      <td id=\"T_be83f_row5_col39\" class=\"data row5 col39\" ></td>\n",
       "      <td id=\"T_be83f_row5_col40\" class=\"data row5 col40\" ></td>\n",
       "      <td id=\"T_be83f_row5_col41\" class=\"data row5 col41\" ></td>\n",
       "      <td id=\"T_be83f_row5_col42\" class=\"data row5 col42\" >11</td>\n",
       "      <td id=\"T_be83f_row5_col43\" class=\"data row5 col43\" ></td>\n",
       "      <td id=\"T_be83f_row5_col44\" class=\"data row5 col44\" >7</td>\n",
       "      <td id=\"T_be83f_row5_col45\" class=\"data row5 col45\" ></td>\n",
       "      <td id=\"T_be83f_row5_col46\" class=\"data row5 col46\" ></td>\n",
       "      <td id=\"T_be83f_row5_col47\" class=\"data row5 col47\" ></td>\n",
       "      <td id=\"T_be83f_row5_col48\" class=\"data row5 col48\" >12</td>\n",
       "      <td id=\"T_be83f_row5_col49\" class=\"data row5 col49\" ></td>\n",
       "      <td id=\"T_be83f_row5_col50\" class=\"data row5 col50\" ></td>\n",
       "      <td id=\"T_be83f_row5_col51\" class=\"data row5 col51\" ></td>\n",
       "      <td id=\"T_be83f_row5_col52\" class=\"data row5 col52\" ></td>\n",
       "      <td id=\"T_be83f_row5_col53\" class=\"data row5 col53\" >4</td>\n",
       "      <td id=\"T_be83f_row5_col54\" class=\"data row5 col54\" ></td>\n",
       "      <td id=\"T_be83f_row5_col55\" class=\"data row5 col55\" ></td>\n",
       "      <td id=\"T_be83f_row5_col56\" class=\"data row5 col56\" ></td>\n",
       "      <td id=\"T_be83f_row5_col57\" class=\"data row5 col57\" >81</td>\n",
       "      <td id=\"T_be83f_row5_col58\" class=\"data row5 col58\" ></td>\n",
       "      <td id=\"T_be83f_row5_col59\" class=\"data row5 col59\" ></td>\n",
       "      <td id=\"T_be83f_row5_col60\" class=\"data row5 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row6\" class=\"row_heading level0 row6\" >LinAlgError</th>\n",
       "      <td id=\"T_be83f_row6_col0\" class=\"data row6 col0\" >21</td>\n",
       "      <td id=\"T_be83f_row6_col1\" class=\"data row6 col1\" >2</td>\n",
       "      <td id=\"T_be83f_row6_col2\" class=\"data row6 col2\" ></td>\n",
       "      <td id=\"T_be83f_row6_col3\" class=\"data row6 col3\" ></td>\n",
       "      <td id=\"T_be83f_row6_col4\" class=\"data row6 col4\" ></td>\n",
       "      <td id=\"T_be83f_row6_col5\" class=\"data row6 col5\" ></td>\n",
       "      <td id=\"T_be83f_row6_col6\" class=\"data row6 col6\" ></td>\n",
       "      <td id=\"T_be83f_row6_col7\" class=\"data row6 col7\" ></td>\n",
       "      <td id=\"T_be83f_row6_col8\" class=\"data row6 col8\" ></td>\n",
       "      <td id=\"T_be83f_row6_col9\" class=\"data row6 col9\" ></td>\n",
       "      <td id=\"T_be83f_row6_col10\" class=\"data row6 col10\" ></td>\n",
       "      <td id=\"T_be83f_row6_col11\" class=\"data row6 col11\" ></td>\n",
       "      <td id=\"T_be83f_row6_col12\" class=\"data row6 col12\" ></td>\n",
       "      <td id=\"T_be83f_row6_col13\" class=\"data row6 col13\" ></td>\n",
       "      <td id=\"T_be83f_row6_col14\" class=\"data row6 col14\" ></td>\n",
       "      <td id=\"T_be83f_row6_col15\" class=\"data row6 col15\" ></td>\n",
       "      <td id=\"T_be83f_row6_col16\" class=\"data row6 col16\" ></td>\n",
       "      <td id=\"T_be83f_row6_col17\" class=\"data row6 col17\" ></td>\n",
       "      <td id=\"T_be83f_row6_col18\" class=\"data row6 col18\" ></td>\n",
       "      <td id=\"T_be83f_row6_col19\" class=\"data row6 col19\" ></td>\n",
       "      <td id=\"T_be83f_row6_col20\" class=\"data row6 col20\" ></td>\n",
       "      <td id=\"T_be83f_row6_col21\" class=\"data row6 col21\" ></td>\n",
       "      <td id=\"T_be83f_row6_col22\" class=\"data row6 col22\" ></td>\n",
       "      <td id=\"T_be83f_row6_col23\" class=\"data row6 col23\" ></td>\n",
       "      <td id=\"T_be83f_row6_col24\" class=\"data row6 col24\" ></td>\n",
       "      <td id=\"T_be83f_row6_col25\" class=\"data row6 col25\" ></td>\n",
       "      <td id=\"T_be83f_row6_col26\" class=\"data row6 col26\" ></td>\n",
       "      <td id=\"T_be83f_row6_col27\" class=\"data row6 col27\" ></td>\n",
       "      <td id=\"T_be83f_row6_col28\" class=\"data row6 col28\" ></td>\n",
       "      <td id=\"T_be83f_row6_col29\" class=\"data row6 col29\" ></td>\n",
       "      <td id=\"T_be83f_row6_col30\" class=\"data row6 col30\" >17</td>\n",
       "      <td id=\"T_be83f_row6_col31\" class=\"data row6 col31\" ></td>\n",
       "      <td id=\"T_be83f_row6_col32\" class=\"data row6 col32\" ></td>\n",
       "      <td id=\"T_be83f_row6_col33\" class=\"data row6 col33\" ></td>\n",
       "      <td id=\"T_be83f_row6_col34\" class=\"data row6 col34\" ></td>\n",
       "      <td id=\"T_be83f_row6_col35\" class=\"data row6 col35\" ></td>\n",
       "      <td id=\"T_be83f_row6_col36\" class=\"data row6 col36\" ></td>\n",
       "      <td id=\"T_be83f_row6_col37\" class=\"data row6 col37\" ></td>\n",
       "      <td id=\"T_be83f_row6_col38\" class=\"data row6 col38\" ></td>\n",
       "      <td id=\"T_be83f_row6_col39\" class=\"data row6 col39\" ></td>\n",
       "      <td id=\"T_be83f_row6_col40\" class=\"data row6 col40\" ></td>\n",
       "      <td id=\"T_be83f_row6_col41\" class=\"data row6 col41\" ></td>\n",
       "      <td id=\"T_be83f_row6_col42\" class=\"data row6 col42\" ></td>\n",
       "      <td id=\"T_be83f_row6_col43\" class=\"data row6 col43\" ></td>\n",
       "      <td id=\"T_be83f_row6_col44\" class=\"data row6 col44\" ></td>\n",
       "      <td id=\"T_be83f_row6_col45\" class=\"data row6 col45\" ></td>\n",
       "      <td id=\"T_be83f_row6_col46\" class=\"data row6 col46\" ></td>\n",
       "      <td id=\"T_be83f_row6_col47\" class=\"data row6 col47\" ></td>\n",
       "      <td id=\"T_be83f_row6_col48\" class=\"data row6 col48\" >2</td>\n",
       "      <td id=\"T_be83f_row6_col49\" class=\"data row6 col49\" ></td>\n",
       "      <td id=\"T_be83f_row6_col50\" class=\"data row6 col50\" ></td>\n",
       "      <td id=\"T_be83f_row6_col51\" class=\"data row6 col51\" ></td>\n",
       "      <td id=\"T_be83f_row6_col52\" class=\"data row6 col52\" ></td>\n",
       "      <td id=\"T_be83f_row6_col53\" class=\"data row6 col53\" ></td>\n",
       "      <td id=\"T_be83f_row6_col54\" class=\"data row6 col54\" ></td>\n",
       "      <td id=\"T_be83f_row6_col55\" class=\"data row6 col55\" ></td>\n",
       "      <td id=\"T_be83f_row6_col56\" class=\"data row6 col56\" ></td>\n",
       "      <td id=\"T_be83f_row6_col57\" class=\"data row6 col57\" ></td>\n",
       "      <td id=\"T_be83f_row6_col58\" class=\"data row6 col58\" ></td>\n",
       "      <td id=\"T_be83f_row6_col59\" class=\"data row6 col59\" ></td>\n",
       "      <td id=\"T_be83f_row6_col60\" class=\"data row6 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row7\" class=\"row_heading level0 row7\" >Max recursion depth exceeded</th>\n",
       "      <td id=\"T_be83f_row7_col0\" class=\"data row7 col0\" >51</td>\n",
       "      <td id=\"T_be83f_row7_col1\" class=\"data row7 col1\" ></td>\n",
       "      <td id=\"T_be83f_row7_col2\" class=\"data row7 col2\" ></td>\n",
       "      <td id=\"T_be83f_row7_col3\" class=\"data row7 col3\" ></td>\n",
       "      <td id=\"T_be83f_row7_col4\" class=\"data row7 col4\" ></td>\n",
       "      <td id=\"T_be83f_row7_col5\" class=\"data row7 col5\" ></td>\n",
       "      <td id=\"T_be83f_row7_col6\" class=\"data row7 col6\" ></td>\n",
       "      <td id=\"T_be83f_row7_col7\" class=\"data row7 col7\" ></td>\n",
       "      <td id=\"T_be83f_row7_col8\" class=\"data row7 col8\" ></td>\n",
       "      <td id=\"T_be83f_row7_col9\" class=\"data row7 col9\" ></td>\n",
       "      <td id=\"T_be83f_row7_col10\" class=\"data row7 col10\" ></td>\n",
       "      <td id=\"T_be83f_row7_col11\" class=\"data row7 col11\" ></td>\n",
       "      <td id=\"T_be83f_row7_col12\" class=\"data row7 col12\" ></td>\n",
       "      <td id=\"T_be83f_row7_col13\" class=\"data row7 col13\" ></td>\n",
       "      <td id=\"T_be83f_row7_col14\" class=\"data row7 col14\" ></td>\n",
       "      <td id=\"T_be83f_row7_col15\" class=\"data row7 col15\" ></td>\n",
       "      <td id=\"T_be83f_row7_col16\" class=\"data row7 col16\" ></td>\n",
       "      <td id=\"T_be83f_row7_col17\" class=\"data row7 col17\" ></td>\n",
       "      <td id=\"T_be83f_row7_col18\" class=\"data row7 col18\" ></td>\n",
       "      <td id=\"T_be83f_row7_col19\" class=\"data row7 col19\" ></td>\n",
       "      <td id=\"T_be83f_row7_col20\" class=\"data row7 col20\" ></td>\n",
       "      <td id=\"T_be83f_row7_col21\" class=\"data row7 col21\" ></td>\n",
       "      <td id=\"T_be83f_row7_col22\" class=\"data row7 col22\" ></td>\n",
       "      <td id=\"T_be83f_row7_col23\" class=\"data row7 col23\" ></td>\n",
       "      <td id=\"T_be83f_row7_col24\" class=\"data row7 col24\" ></td>\n",
       "      <td id=\"T_be83f_row7_col25\" class=\"data row7 col25\" ></td>\n",
       "      <td id=\"T_be83f_row7_col26\" class=\"data row7 col26\" ></td>\n",
       "      <td id=\"T_be83f_row7_col27\" class=\"data row7 col27\" ></td>\n",
       "      <td id=\"T_be83f_row7_col28\" class=\"data row7 col28\" ></td>\n",
       "      <td id=\"T_be83f_row7_col29\" class=\"data row7 col29\" ></td>\n",
       "      <td id=\"T_be83f_row7_col30\" class=\"data row7 col30\" ></td>\n",
       "      <td id=\"T_be83f_row7_col31\" class=\"data row7 col31\" ></td>\n",
       "      <td id=\"T_be83f_row7_col32\" class=\"data row7 col32\" ></td>\n",
       "      <td id=\"T_be83f_row7_col33\" class=\"data row7 col33\" ></td>\n",
       "      <td id=\"T_be83f_row7_col34\" class=\"data row7 col34\" ></td>\n",
       "      <td id=\"T_be83f_row7_col35\" class=\"data row7 col35\" ></td>\n",
       "      <td id=\"T_be83f_row7_col36\" class=\"data row7 col36\" ></td>\n",
       "      <td id=\"T_be83f_row7_col37\" class=\"data row7 col37\" ></td>\n",
       "      <td id=\"T_be83f_row7_col38\" class=\"data row7 col38\" ></td>\n",
       "      <td id=\"T_be83f_row7_col39\" class=\"data row7 col39\" ></td>\n",
       "      <td id=\"T_be83f_row7_col40\" class=\"data row7 col40\" ></td>\n",
       "      <td id=\"T_be83f_row7_col41\" class=\"data row7 col41\" ></td>\n",
       "      <td id=\"T_be83f_row7_col42\" class=\"data row7 col42\" ></td>\n",
       "      <td id=\"T_be83f_row7_col43\" class=\"data row7 col43\" ></td>\n",
       "      <td id=\"T_be83f_row7_col44\" class=\"data row7 col44\" ></td>\n",
       "      <td id=\"T_be83f_row7_col45\" class=\"data row7 col45\" ></td>\n",
       "      <td id=\"T_be83f_row7_col46\" class=\"data row7 col46\" ></td>\n",
       "      <td id=\"T_be83f_row7_col47\" class=\"data row7 col47\" ></td>\n",
       "      <td id=\"T_be83f_row7_col48\" class=\"data row7 col48\" ></td>\n",
       "      <td id=\"T_be83f_row7_col49\" class=\"data row7 col49\" ></td>\n",
       "      <td id=\"T_be83f_row7_col50\" class=\"data row7 col50\" ></td>\n",
       "      <td id=\"T_be83f_row7_col51\" class=\"data row7 col51\" ></td>\n",
       "      <td id=\"T_be83f_row7_col52\" class=\"data row7 col52\" >51</td>\n",
       "      <td id=\"T_be83f_row7_col53\" class=\"data row7 col53\" ></td>\n",
       "      <td id=\"T_be83f_row7_col54\" class=\"data row7 col54\" ></td>\n",
       "      <td id=\"T_be83f_row7_col55\" class=\"data row7 col55\" ></td>\n",
       "      <td id=\"T_be83f_row7_col56\" class=\"data row7 col56\" ></td>\n",
       "      <td id=\"T_be83f_row7_col57\" class=\"data row7 col57\" ></td>\n",
       "      <td id=\"T_be83f_row7_col58\" class=\"data row7 col58\" ></td>\n",
       "      <td id=\"T_be83f_row7_col59\" class=\"data row7 col59\" ></td>\n",
       "      <td id=\"T_be83f_row7_col60\" class=\"data row7 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row8\" class=\"row_heading level0 row8\" >Model loading error</th>\n",
       "      <td id=\"T_be83f_row8_col0\" class=\"data row8 col0\" >9</td>\n",
       "      <td id=\"T_be83f_row8_col1\" class=\"data row8 col1\" ></td>\n",
       "      <td id=\"T_be83f_row8_col2\" class=\"data row8 col2\" ></td>\n",
       "      <td id=\"T_be83f_row8_col3\" class=\"data row8 col3\" ></td>\n",
       "      <td id=\"T_be83f_row8_col4\" class=\"data row8 col4\" ></td>\n",
       "      <td id=\"T_be83f_row8_col5\" class=\"data row8 col5\" ></td>\n",
       "      <td id=\"T_be83f_row8_col6\" class=\"data row8 col6\" ></td>\n",
       "      <td id=\"T_be83f_row8_col7\" class=\"data row8 col7\" ></td>\n",
       "      <td id=\"T_be83f_row8_col8\" class=\"data row8 col8\" ></td>\n",
       "      <td id=\"T_be83f_row8_col9\" class=\"data row8 col9\" ></td>\n",
       "      <td id=\"T_be83f_row8_col10\" class=\"data row8 col10\" ></td>\n",
       "      <td id=\"T_be83f_row8_col11\" class=\"data row8 col11\" ></td>\n",
       "      <td id=\"T_be83f_row8_col12\" class=\"data row8 col12\" ></td>\n",
       "      <td id=\"T_be83f_row8_col13\" class=\"data row8 col13\" ></td>\n",
       "      <td id=\"T_be83f_row8_col14\" class=\"data row8 col14\" ></td>\n",
       "      <td id=\"T_be83f_row8_col15\" class=\"data row8 col15\" ></td>\n",
       "      <td id=\"T_be83f_row8_col16\" class=\"data row8 col16\" ></td>\n",
       "      <td id=\"T_be83f_row8_col17\" class=\"data row8 col17\" ></td>\n",
       "      <td id=\"T_be83f_row8_col18\" class=\"data row8 col18\" ></td>\n",
       "      <td id=\"T_be83f_row8_col19\" class=\"data row8 col19\" ></td>\n",
       "      <td id=\"T_be83f_row8_col20\" class=\"data row8 col20\" ></td>\n",
       "      <td id=\"T_be83f_row8_col21\" class=\"data row8 col21\" ></td>\n",
       "      <td id=\"T_be83f_row8_col22\" class=\"data row8 col22\" ></td>\n",
       "      <td id=\"T_be83f_row8_col23\" class=\"data row8 col23\" ></td>\n",
       "      <td id=\"T_be83f_row8_col24\" class=\"data row8 col24\" ></td>\n",
       "      <td id=\"T_be83f_row8_col25\" class=\"data row8 col25\" ></td>\n",
       "      <td id=\"T_be83f_row8_col26\" class=\"data row8 col26\" ></td>\n",
       "      <td id=\"T_be83f_row8_col27\" class=\"data row8 col27\" ></td>\n",
       "      <td id=\"T_be83f_row8_col28\" class=\"data row8 col28\" ></td>\n",
       "      <td id=\"T_be83f_row8_col29\" class=\"data row8 col29\" ></td>\n",
       "      <td id=\"T_be83f_row8_col30\" class=\"data row8 col30\" ></td>\n",
       "      <td id=\"T_be83f_row8_col31\" class=\"data row8 col31\" ></td>\n",
       "      <td id=\"T_be83f_row8_col32\" class=\"data row8 col32\" ></td>\n",
       "      <td id=\"T_be83f_row8_col33\" class=\"data row8 col33\" ></td>\n",
       "      <td id=\"T_be83f_row8_col34\" class=\"data row8 col34\" >9</td>\n",
       "      <td id=\"T_be83f_row8_col35\" class=\"data row8 col35\" ></td>\n",
       "      <td id=\"T_be83f_row8_col36\" class=\"data row8 col36\" ></td>\n",
       "      <td id=\"T_be83f_row8_col37\" class=\"data row8 col37\" ></td>\n",
       "      <td id=\"T_be83f_row8_col38\" class=\"data row8 col38\" ></td>\n",
       "      <td id=\"T_be83f_row8_col39\" class=\"data row8 col39\" ></td>\n",
       "      <td id=\"T_be83f_row8_col40\" class=\"data row8 col40\" ></td>\n",
       "      <td id=\"T_be83f_row8_col41\" class=\"data row8 col41\" ></td>\n",
       "      <td id=\"T_be83f_row8_col42\" class=\"data row8 col42\" ></td>\n",
       "      <td id=\"T_be83f_row8_col43\" class=\"data row8 col43\" ></td>\n",
       "      <td id=\"T_be83f_row8_col44\" class=\"data row8 col44\" ></td>\n",
       "      <td id=\"T_be83f_row8_col45\" class=\"data row8 col45\" ></td>\n",
       "      <td id=\"T_be83f_row8_col46\" class=\"data row8 col46\" ></td>\n",
       "      <td id=\"T_be83f_row8_col47\" class=\"data row8 col47\" ></td>\n",
       "      <td id=\"T_be83f_row8_col48\" class=\"data row8 col48\" ></td>\n",
       "      <td id=\"T_be83f_row8_col49\" class=\"data row8 col49\" ></td>\n",
       "      <td id=\"T_be83f_row8_col50\" class=\"data row8 col50\" ></td>\n",
       "      <td id=\"T_be83f_row8_col51\" class=\"data row8 col51\" ></td>\n",
       "      <td id=\"T_be83f_row8_col52\" class=\"data row8 col52\" ></td>\n",
       "      <td id=\"T_be83f_row8_col53\" class=\"data row8 col53\" ></td>\n",
       "      <td id=\"T_be83f_row8_col54\" class=\"data row8 col54\" ></td>\n",
       "      <td id=\"T_be83f_row8_col55\" class=\"data row8 col55\" ></td>\n",
       "      <td id=\"T_be83f_row8_col56\" class=\"data row8 col56\" ></td>\n",
       "      <td id=\"T_be83f_row8_col57\" class=\"data row8 col57\" ></td>\n",
       "      <td id=\"T_be83f_row8_col58\" class=\"data row8 col58\" ></td>\n",
       "      <td id=\"T_be83f_row8_col59\" class=\"data row8 col59\" ></td>\n",
       "      <td id=\"T_be83f_row8_col60\" class=\"data row8 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row9\" class=\"row_heading level0 row9\" >Not converged</th>\n",
       "      <td id=\"T_be83f_row9_col0\" class=\"data row9 col0\" >106</td>\n",
       "      <td id=\"T_be83f_row9_col1\" class=\"data row9 col1\" ></td>\n",
       "      <td id=\"T_be83f_row9_col2\" class=\"data row9 col2\" ></td>\n",
       "      <td id=\"T_be83f_row9_col3\" class=\"data row9 col3\" >3</td>\n",
       "      <td id=\"T_be83f_row9_col4\" class=\"data row9 col4\" ></td>\n",
       "      <td id=\"T_be83f_row9_col5\" class=\"data row9 col5\" ></td>\n",
       "      <td id=\"T_be83f_row9_col6\" class=\"data row9 col6\" ></td>\n",
       "      <td id=\"T_be83f_row9_col7\" class=\"data row9 col7\" ></td>\n",
       "      <td id=\"T_be83f_row9_col8\" class=\"data row9 col8\" ></td>\n",
       "      <td id=\"T_be83f_row9_col9\" class=\"data row9 col9\" ></td>\n",
       "      <td id=\"T_be83f_row9_col10\" class=\"data row9 col10\" ></td>\n",
       "      <td id=\"T_be83f_row9_col11\" class=\"data row9 col11\" ></td>\n",
       "      <td id=\"T_be83f_row9_col12\" class=\"data row9 col12\" ></td>\n",
       "      <td id=\"T_be83f_row9_col13\" class=\"data row9 col13\" ></td>\n",
       "      <td id=\"T_be83f_row9_col14\" class=\"data row9 col14\" ></td>\n",
       "      <td id=\"T_be83f_row9_col15\" class=\"data row9 col15\" ></td>\n",
       "      <td id=\"T_be83f_row9_col16\" class=\"data row9 col16\" ></td>\n",
       "      <td id=\"T_be83f_row9_col17\" class=\"data row9 col17\" ></td>\n",
       "      <td id=\"T_be83f_row9_col18\" class=\"data row9 col18\" ></td>\n",
       "      <td id=\"T_be83f_row9_col19\" class=\"data row9 col19\" ></td>\n",
       "      <td id=\"T_be83f_row9_col20\" class=\"data row9 col20\" ></td>\n",
       "      <td id=\"T_be83f_row9_col21\" class=\"data row9 col21\" ></td>\n",
       "      <td id=\"T_be83f_row9_col22\" class=\"data row9 col22\" ></td>\n",
       "      <td id=\"T_be83f_row9_col23\" class=\"data row9 col23\" ></td>\n",
       "      <td id=\"T_be83f_row9_col24\" class=\"data row9 col24\" ></td>\n",
       "      <td id=\"T_be83f_row9_col25\" class=\"data row9 col25\" ></td>\n",
       "      <td id=\"T_be83f_row9_col26\" class=\"data row9 col26\" ></td>\n",
       "      <td id=\"T_be83f_row9_col27\" class=\"data row9 col27\" ></td>\n",
       "      <td id=\"T_be83f_row9_col28\" class=\"data row9 col28\" ></td>\n",
       "      <td id=\"T_be83f_row9_col29\" class=\"data row9 col29\" >103</td>\n",
       "      <td id=\"T_be83f_row9_col30\" class=\"data row9 col30\" ></td>\n",
       "      <td id=\"T_be83f_row9_col31\" class=\"data row9 col31\" ></td>\n",
       "      <td id=\"T_be83f_row9_col32\" class=\"data row9 col32\" ></td>\n",
       "      <td id=\"T_be83f_row9_col33\" class=\"data row9 col33\" ></td>\n",
       "      <td id=\"T_be83f_row9_col34\" class=\"data row9 col34\" ></td>\n",
       "      <td id=\"T_be83f_row9_col35\" class=\"data row9 col35\" ></td>\n",
       "      <td id=\"T_be83f_row9_col36\" class=\"data row9 col36\" ></td>\n",
       "      <td id=\"T_be83f_row9_col37\" class=\"data row9 col37\" ></td>\n",
       "      <td id=\"T_be83f_row9_col38\" class=\"data row9 col38\" ></td>\n",
       "      <td id=\"T_be83f_row9_col39\" class=\"data row9 col39\" ></td>\n",
       "      <td id=\"T_be83f_row9_col40\" class=\"data row9 col40\" ></td>\n",
       "      <td id=\"T_be83f_row9_col41\" class=\"data row9 col41\" ></td>\n",
       "      <td id=\"T_be83f_row9_col42\" class=\"data row9 col42\" ></td>\n",
       "      <td id=\"T_be83f_row9_col43\" class=\"data row9 col43\" ></td>\n",
       "      <td id=\"T_be83f_row9_col44\" class=\"data row9 col44\" ></td>\n",
       "      <td id=\"T_be83f_row9_col45\" class=\"data row9 col45\" ></td>\n",
       "      <td id=\"T_be83f_row9_col46\" class=\"data row9 col46\" ></td>\n",
       "      <td id=\"T_be83f_row9_col47\" class=\"data row9 col47\" ></td>\n",
       "      <td id=\"T_be83f_row9_col48\" class=\"data row9 col48\" ></td>\n",
       "      <td id=\"T_be83f_row9_col49\" class=\"data row9 col49\" ></td>\n",
       "      <td id=\"T_be83f_row9_col50\" class=\"data row9 col50\" ></td>\n",
       "      <td id=\"T_be83f_row9_col51\" class=\"data row9 col51\" ></td>\n",
       "      <td id=\"T_be83f_row9_col52\" class=\"data row9 col52\" ></td>\n",
       "      <td id=\"T_be83f_row9_col53\" class=\"data row9 col53\" ></td>\n",
       "      <td id=\"T_be83f_row9_col54\" class=\"data row9 col54\" ></td>\n",
       "      <td id=\"T_be83f_row9_col55\" class=\"data row9 col55\" ></td>\n",
       "      <td id=\"T_be83f_row9_col56\" class=\"data row9 col56\" ></td>\n",
       "      <td id=\"T_be83f_row9_col57\" class=\"data row9 col57\" ></td>\n",
       "      <td id=\"T_be83f_row9_col58\" class=\"data row9 col58\" ></td>\n",
       "      <td id=\"T_be83f_row9_col59\" class=\"data row9 col59\" ></td>\n",
       "      <td id=\"T_be83f_row9_col60\" class=\"data row9 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row10\" class=\"row_heading level0 row10\" >TimeEval:IndexError</th>\n",
       "      <td id=\"T_be83f_row10_col0\" class=\"data row10 col0\" >3</td>\n",
       "      <td id=\"T_be83f_row10_col1\" class=\"data row10 col1\" ></td>\n",
       "      <td id=\"T_be83f_row10_col2\" class=\"data row10 col2\" ></td>\n",
       "      <td id=\"T_be83f_row10_col3\" class=\"data row10 col3\" ></td>\n",
       "      <td id=\"T_be83f_row10_col4\" class=\"data row10 col4\" ></td>\n",
       "      <td id=\"T_be83f_row10_col5\" class=\"data row10 col5\" ></td>\n",
       "      <td id=\"T_be83f_row10_col6\" class=\"data row10 col6\" ></td>\n",
       "      <td id=\"T_be83f_row10_col7\" class=\"data row10 col7\" ></td>\n",
       "      <td id=\"T_be83f_row10_col8\" class=\"data row10 col8\" ></td>\n",
       "      <td id=\"T_be83f_row10_col9\" class=\"data row10 col9\" >3</td>\n",
       "      <td id=\"T_be83f_row10_col10\" class=\"data row10 col10\" ></td>\n",
       "      <td id=\"T_be83f_row10_col11\" class=\"data row10 col11\" ></td>\n",
       "      <td id=\"T_be83f_row10_col12\" class=\"data row10 col12\" ></td>\n",
       "      <td id=\"T_be83f_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "      <td id=\"T_be83f_row10_col14\" class=\"data row10 col14\" ></td>\n",
       "      <td id=\"T_be83f_row10_col15\" class=\"data row10 col15\" ></td>\n",
       "      <td id=\"T_be83f_row10_col16\" class=\"data row10 col16\" ></td>\n",
       "      <td id=\"T_be83f_row10_col17\" class=\"data row10 col17\" ></td>\n",
       "      <td id=\"T_be83f_row10_col18\" class=\"data row10 col18\" ></td>\n",
       "      <td id=\"T_be83f_row10_col19\" class=\"data row10 col19\" ></td>\n",
       "      <td id=\"T_be83f_row10_col20\" class=\"data row10 col20\" ></td>\n",
       "      <td id=\"T_be83f_row10_col21\" class=\"data row10 col21\" ></td>\n",
       "      <td id=\"T_be83f_row10_col22\" class=\"data row10 col22\" ></td>\n",
       "      <td id=\"T_be83f_row10_col23\" class=\"data row10 col23\" ></td>\n",
       "      <td id=\"T_be83f_row10_col24\" class=\"data row10 col24\" ></td>\n",
       "      <td id=\"T_be83f_row10_col25\" class=\"data row10 col25\" ></td>\n",
       "      <td id=\"T_be83f_row10_col26\" class=\"data row10 col26\" ></td>\n",
       "      <td id=\"T_be83f_row10_col27\" class=\"data row10 col27\" ></td>\n",
       "      <td id=\"T_be83f_row10_col28\" class=\"data row10 col28\" ></td>\n",
       "      <td id=\"T_be83f_row10_col29\" class=\"data row10 col29\" ></td>\n",
       "      <td id=\"T_be83f_row10_col30\" class=\"data row10 col30\" ></td>\n",
       "      <td id=\"T_be83f_row10_col31\" class=\"data row10 col31\" ></td>\n",
       "      <td id=\"T_be83f_row10_col32\" class=\"data row10 col32\" ></td>\n",
       "      <td id=\"T_be83f_row10_col33\" class=\"data row10 col33\" ></td>\n",
       "      <td id=\"T_be83f_row10_col34\" class=\"data row10 col34\" ></td>\n",
       "      <td id=\"T_be83f_row10_col35\" class=\"data row10 col35\" ></td>\n",
       "      <td id=\"T_be83f_row10_col36\" class=\"data row10 col36\" ></td>\n",
       "      <td id=\"T_be83f_row10_col37\" class=\"data row10 col37\" ></td>\n",
       "      <td id=\"T_be83f_row10_col38\" class=\"data row10 col38\" ></td>\n",
       "      <td id=\"T_be83f_row10_col39\" class=\"data row10 col39\" ></td>\n",
       "      <td id=\"T_be83f_row10_col40\" class=\"data row10 col40\" ></td>\n",
       "      <td id=\"T_be83f_row10_col41\" class=\"data row10 col41\" ></td>\n",
       "      <td id=\"T_be83f_row10_col42\" class=\"data row10 col42\" ></td>\n",
       "      <td id=\"T_be83f_row10_col43\" class=\"data row10 col43\" ></td>\n",
       "      <td id=\"T_be83f_row10_col44\" class=\"data row10 col44\" ></td>\n",
       "      <td id=\"T_be83f_row10_col45\" class=\"data row10 col45\" ></td>\n",
       "      <td id=\"T_be83f_row10_col46\" class=\"data row10 col46\" ></td>\n",
       "      <td id=\"T_be83f_row10_col47\" class=\"data row10 col47\" ></td>\n",
       "      <td id=\"T_be83f_row10_col48\" class=\"data row10 col48\" ></td>\n",
       "      <td id=\"T_be83f_row10_col49\" class=\"data row10 col49\" ></td>\n",
       "      <td id=\"T_be83f_row10_col50\" class=\"data row10 col50\" ></td>\n",
       "      <td id=\"T_be83f_row10_col51\" class=\"data row10 col51\" ></td>\n",
       "      <td id=\"T_be83f_row10_col52\" class=\"data row10 col52\" ></td>\n",
       "      <td id=\"T_be83f_row10_col53\" class=\"data row10 col53\" ></td>\n",
       "      <td id=\"T_be83f_row10_col54\" class=\"data row10 col54\" ></td>\n",
       "      <td id=\"T_be83f_row10_col55\" class=\"data row10 col55\" ></td>\n",
       "      <td id=\"T_be83f_row10_col56\" class=\"data row10 col56\" ></td>\n",
       "      <td id=\"T_be83f_row10_col57\" class=\"data row10 col57\" ></td>\n",
       "      <td id=\"T_be83f_row10_col58\" class=\"data row10 col58\" ></td>\n",
       "      <td id=\"T_be83f_row10_col59\" class=\"data row10 col59\" ></td>\n",
       "      <td id=\"T_be83f_row10_col60\" class=\"data row10 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row11\" class=\"row_heading level0 row11\" >Wrong shape error</th>\n",
       "      <td id=\"T_be83f_row11_col0\" class=\"data row11 col0\" >17</td>\n",
       "      <td id=\"T_be83f_row11_col1\" class=\"data row11 col1\" ></td>\n",
       "      <td id=\"T_be83f_row11_col2\" class=\"data row11 col2\" ></td>\n",
       "      <td id=\"T_be83f_row11_col3\" class=\"data row11 col3\" ></td>\n",
       "      <td id=\"T_be83f_row11_col4\" class=\"data row11 col4\" ></td>\n",
       "      <td id=\"T_be83f_row11_col5\" class=\"data row11 col5\" ></td>\n",
       "      <td id=\"T_be83f_row11_col6\" class=\"data row11 col6\" ></td>\n",
       "      <td id=\"T_be83f_row11_col7\" class=\"data row11 col7\" ></td>\n",
       "      <td id=\"T_be83f_row11_col8\" class=\"data row11 col8\" ></td>\n",
       "      <td id=\"T_be83f_row11_col9\" class=\"data row11 col9\" >2</td>\n",
       "      <td id=\"T_be83f_row11_col10\" class=\"data row11 col10\" ></td>\n",
       "      <td id=\"T_be83f_row11_col11\" class=\"data row11 col11\" ></td>\n",
       "      <td id=\"T_be83f_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_be83f_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "      <td id=\"T_be83f_row11_col14\" class=\"data row11 col14\" ></td>\n",
       "      <td id=\"T_be83f_row11_col15\" class=\"data row11 col15\" ></td>\n",
       "      <td id=\"T_be83f_row11_col16\" class=\"data row11 col16\" ></td>\n",
       "      <td id=\"T_be83f_row11_col17\" class=\"data row11 col17\" ></td>\n",
       "      <td id=\"T_be83f_row11_col18\" class=\"data row11 col18\" ></td>\n",
       "      <td id=\"T_be83f_row11_col19\" class=\"data row11 col19\" ></td>\n",
       "      <td id=\"T_be83f_row11_col20\" class=\"data row11 col20\" >1</td>\n",
       "      <td id=\"T_be83f_row11_col21\" class=\"data row11 col21\" ></td>\n",
       "      <td id=\"T_be83f_row11_col22\" class=\"data row11 col22\" ></td>\n",
       "      <td id=\"T_be83f_row11_col23\" class=\"data row11 col23\" ></td>\n",
       "      <td id=\"T_be83f_row11_col24\" class=\"data row11 col24\" ></td>\n",
       "      <td id=\"T_be83f_row11_col25\" class=\"data row11 col25\" ></td>\n",
       "      <td id=\"T_be83f_row11_col26\" class=\"data row11 col26\" ></td>\n",
       "      <td id=\"T_be83f_row11_col27\" class=\"data row11 col27\" ></td>\n",
       "      <td id=\"T_be83f_row11_col28\" class=\"data row11 col28\" ></td>\n",
       "      <td id=\"T_be83f_row11_col29\" class=\"data row11 col29\" ></td>\n",
       "      <td id=\"T_be83f_row11_col30\" class=\"data row11 col30\" ></td>\n",
       "      <td id=\"T_be83f_row11_col31\" class=\"data row11 col31\" ></td>\n",
       "      <td id=\"T_be83f_row11_col32\" class=\"data row11 col32\" ></td>\n",
       "      <td id=\"T_be83f_row11_col33\" class=\"data row11 col33\" ></td>\n",
       "      <td id=\"T_be83f_row11_col34\" class=\"data row11 col34\" ></td>\n",
       "      <td id=\"T_be83f_row11_col35\" class=\"data row11 col35\" ></td>\n",
       "      <td id=\"T_be83f_row11_col36\" class=\"data row11 col36\" ></td>\n",
       "      <td id=\"T_be83f_row11_col37\" class=\"data row11 col37\" ></td>\n",
       "      <td id=\"T_be83f_row11_col38\" class=\"data row11 col38\" >3</td>\n",
       "      <td id=\"T_be83f_row11_col39\" class=\"data row11 col39\" ></td>\n",
       "      <td id=\"T_be83f_row11_col40\" class=\"data row11 col40\" ></td>\n",
       "      <td id=\"T_be83f_row11_col41\" class=\"data row11 col41\" ></td>\n",
       "      <td id=\"T_be83f_row11_col42\" class=\"data row11 col42\" ></td>\n",
       "      <td id=\"T_be83f_row11_col43\" class=\"data row11 col43\" >11</td>\n",
       "      <td id=\"T_be83f_row11_col44\" class=\"data row11 col44\" ></td>\n",
       "      <td id=\"T_be83f_row11_col45\" class=\"data row11 col45\" ></td>\n",
       "      <td id=\"T_be83f_row11_col46\" class=\"data row11 col46\" ></td>\n",
       "      <td id=\"T_be83f_row11_col47\" class=\"data row11 col47\" ></td>\n",
       "      <td id=\"T_be83f_row11_col48\" class=\"data row11 col48\" ></td>\n",
       "      <td id=\"T_be83f_row11_col49\" class=\"data row11 col49\" ></td>\n",
       "      <td id=\"T_be83f_row11_col50\" class=\"data row11 col50\" ></td>\n",
       "      <td id=\"T_be83f_row11_col51\" class=\"data row11 col51\" ></td>\n",
       "      <td id=\"T_be83f_row11_col52\" class=\"data row11 col52\" ></td>\n",
       "      <td id=\"T_be83f_row11_col53\" class=\"data row11 col53\" ></td>\n",
       "      <td id=\"T_be83f_row11_col54\" class=\"data row11 col54\" ></td>\n",
       "      <td id=\"T_be83f_row11_col55\" class=\"data row11 col55\" ></td>\n",
       "      <td id=\"T_be83f_row11_col56\" class=\"data row11 col56\" ></td>\n",
       "      <td id=\"T_be83f_row11_col57\" class=\"data row11 col57\" ></td>\n",
       "      <td id=\"T_be83f_row11_col58\" class=\"data row11 col58\" ></td>\n",
       "      <td id=\"T_be83f_row11_col59\" class=\"data row11 col59\" ></td>\n",
       "      <td id=\"T_be83f_row11_col60\" class=\"data row11 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row12\" class=\"row_heading level0 row12\" >other</th>\n",
       "      <td id=\"T_be83f_row12_col0\" class=\"data row12 col0\" >20</td>\n",
       "      <td id=\"T_be83f_row12_col1\" class=\"data row12 col1\" >1</td>\n",
       "      <td id=\"T_be83f_row12_col2\" class=\"data row12 col2\" ></td>\n",
       "      <td id=\"T_be83f_row12_col3\" class=\"data row12 col3\" ></td>\n",
       "      <td id=\"T_be83f_row12_col4\" class=\"data row12 col4\" ></td>\n",
       "      <td id=\"T_be83f_row12_col5\" class=\"data row12 col5\" ></td>\n",
       "      <td id=\"T_be83f_row12_col6\" class=\"data row12 col6\" >13</td>\n",
       "      <td id=\"T_be83f_row12_col7\" class=\"data row12 col7\" ></td>\n",
       "      <td id=\"T_be83f_row12_col8\" class=\"data row12 col8\" ></td>\n",
       "      <td id=\"T_be83f_row12_col9\" class=\"data row12 col9\" ></td>\n",
       "      <td id=\"T_be83f_row12_col10\" class=\"data row12 col10\" ></td>\n",
       "      <td id=\"T_be83f_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_be83f_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_be83f_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "      <td id=\"T_be83f_row12_col14\" class=\"data row12 col14\" ></td>\n",
       "      <td id=\"T_be83f_row12_col15\" class=\"data row12 col15\" ></td>\n",
       "      <td id=\"T_be83f_row12_col16\" class=\"data row12 col16\" ></td>\n",
       "      <td id=\"T_be83f_row12_col17\" class=\"data row12 col17\" ></td>\n",
       "      <td id=\"T_be83f_row12_col18\" class=\"data row12 col18\" ></td>\n",
       "      <td id=\"T_be83f_row12_col19\" class=\"data row12 col19\" ></td>\n",
       "      <td id=\"T_be83f_row12_col20\" class=\"data row12 col20\" ></td>\n",
       "      <td id=\"T_be83f_row12_col21\" class=\"data row12 col21\" ></td>\n",
       "      <td id=\"T_be83f_row12_col22\" class=\"data row12 col22\" ></td>\n",
       "      <td id=\"T_be83f_row12_col23\" class=\"data row12 col23\" ></td>\n",
       "      <td id=\"T_be83f_row12_col24\" class=\"data row12 col24\" ></td>\n",
       "      <td id=\"T_be83f_row12_col25\" class=\"data row12 col25\" ></td>\n",
       "      <td id=\"T_be83f_row12_col26\" class=\"data row12 col26\" ></td>\n",
       "      <td id=\"T_be83f_row12_col27\" class=\"data row12 col27\" ></td>\n",
       "      <td id=\"T_be83f_row12_col28\" class=\"data row12 col28\" >1</td>\n",
       "      <td id=\"T_be83f_row12_col29\" class=\"data row12 col29\" ></td>\n",
       "      <td id=\"T_be83f_row12_col30\" class=\"data row12 col30\" ></td>\n",
       "      <td id=\"T_be83f_row12_col31\" class=\"data row12 col31\" ></td>\n",
       "      <td id=\"T_be83f_row12_col32\" class=\"data row12 col32\" ></td>\n",
       "      <td id=\"T_be83f_row12_col33\" class=\"data row12 col33\" >3</td>\n",
       "      <td id=\"T_be83f_row12_col34\" class=\"data row12 col34\" >2</td>\n",
       "      <td id=\"T_be83f_row12_col35\" class=\"data row12 col35\" ></td>\n",
       "      <td id=\"T_be83f_row12_col36\" class=\"data row12 col36\" ></td>\n",
       "      <td id=\"T_be83f_row12_col37\" class=\"data row12 col37\" ></td>\n",
       "      <td id=\"T_be83f_row12_col38\" class=\"data row12 col38\" ></td>\n",
       "      <td id=\"T_be83f_row12_col39\" class=\"data row12 col39\" ></td>\n",
       "      <td id=\"T_be83f_row12_col40\" class=\"data row12 col40\" ></td>\n",
       "      <td id=\"T_be83f_row12_col41\" class=\"data row12 col41\" ></td>\n",
       "      <td id=\"T_be83f_row12_col42\" class=\"data row12 col42\" ></td>\n",
       "      <td id=\"T_be83f_row12_col43\" class=\"data row12 col43\" ></td>\n",
       "      <td id=\"T_be83f_row12_col44\" class=\"data row12 col44\" ></td>\n",
       "      <td id=\"T_be83f_row12_col45\" class=\"data row12 col45\" ></td>\n",
       "      <td id=\"T_be83f_row12_col46\" class=\"data row12 col46\" ></td>\n",
       "      <td id=\"T_be83f_row12_col47\" class=\"data row12 col47\" ></td>\n",
       "      <td id=\"T_be83f_row12_col48\" class=\"data row12 col48\" ></td>\n",
       "      <td id=\"T_be83f_row12_col49\" class=\"data row12 col49\" ></td>\n",
       "      <td id=\"T_be83f_row12_col50\" class=\"data row12 col50\" ></td>\n",
       "      <td id=\"T_be83f_row12_col51\" class=\"data row12 col51\" ></td>\n",
       "      <td id=\"T_be83f_row12_col52\" class=\"data row12 col52\" ></td>\n",
       "      <td id=\"T_be83f_row12_col53\" class=\"data row12 col53\" ></td>\n",
       "      <td id=\"T_be83f_row12_col54\" class=\"data row12 col54\" ></td>\n",
       "      <td id=\"T_be83f_row12_col55\" class=\"data row12 col55\" ></td>\n",
       "      <td id=\"T_be83f_row12_col56\" class=\"data row12 col56\" ></td>\n",
       "      <td id=\"T_be83f_row12_col57\" class=\"data row12 col57\" ></td>\n",
       "      <td id=\"T_be83f_row12_col58\" class=\"data row12 col58\" ></td>\n",
       "      <td id=\"T_be83f_row12_col59\" class=\"data row12 col59\" ></td>\n",
       "      <td id=\"T_be83f_row12_col60\" class=\"data row12 col60\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_be83f_level0_row13\" class=\"row_heading level0 row13\" >unexpected Inf or NaN</th>\n",
       "      <td id=\"T_be83f_row13_col0\" class=\"data row13 col0\" >226</td>\n",
       "      <td id=\"T_be83f_row13_col1\" class=\"data row13 col1\" >1</td>\n",
       "      <td id=\"T_be83f_row13_col2\" class=\"data row13 col2\" ></td>\n",
       "      <td id=\"T_be83f_row13_col3\" class=\"data row13 col3\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col4\" class=\"data row13 col4\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col5\" class=\"data row13 col5\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "      <td id=\"T_be83f_row13_col7\" class=\"data row13 col7\" ></td>\n",
       "      <td id=\"T_be83f_row13_col8\" class=\"data row13 col8\" ></td>\n",
       "      <td id=\"T_be83f_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "      <td id=\"T_be83f_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_be83f_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_be83f_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_be83f_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "      <td id=\"T_be83f_row13_col14\" class=\"data row13 col14\" ></td>\n",
       "      <td id=\"T_be83f_row13_col15\" class=\"data row13 col15\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col16\" class=\"data row13 col16\" ></td>\n",
       "      <td id=\"T_be83f_row13_col17\" class=\"data row13 col17\" ></td>\n",
       "      <td id=\"T_be83f_row13_col18\" class=\"data row13 col18\" ></td>\n",
       "      <td id=\"T_be83f_row13_col19\" class=\"data row13 col19\" ></td>\n",
       "      <td id=\"T_be83f_row13_col20\" class=\"data row13 col20\" >45</td>\n",
       "      <td id=\"T_be83f_row13_col21\" class=\"data row13 col21\" ></td>\n",
       "      <td id=\"T_be83f_row13_col22\" class=\"data row13 col22\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col23\" class=\"data row13 col23\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col24\" class=\"data row13 col24\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col25\" class=\"data row13 col25\" ></td>\n",
       "      <td id=\"T_be83f_row13_col26\" class=\"data row13 col26\" ></td>\n",
       "      <td id=\"T_be83f_row13_col27\" class=\"data row13 col27\" ></td>\n",
       "      <td id=\"T_be83f_row13_col28\" class=\"data row13 col28\" ></td>\n",
       "      <td id=\"T_be83f_row13_col29\" class=\"data row13 col29\" ></td>\n",
       "      <td id=\"T_be83f_row13_col30\" class=\"data row13 col30\" ></td>\n",
       "      <td id=\"T_be83f_row13_col31\" class=\"data row13 col31\" ></td>\n",
       "      <td id=\"T_be83f_row13_col32\" class=\"data row13 col32\" ></td>\n",
       "      <td id=\"T_be83f_row13_col33\" class=\"data row13 col33\" ></td>\n",
       "      <td id=\"T_be83f_row13_col34\" class=\"data row13 col34\" ></td>\n",
       "      <td id=\"T_be83f_row13_col35\" class=\"data row13 col35\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col36\" class=\"data row13 col36\" ></td>\n",
       "      <td id=\"T_be83f_row13_col37\" class=\"data row13 col37\" ></td>\n",
       "      <td id=\"T_be83f_row13_col38\" class=\"data row13 col38\" ></td>\n",
       "      <td id=\"T_be83f_row13_col39\" class=\"data row13 col39\" ></td>\n",
       "      <td id=\"T_be83f_row13_col40\" class=\"data row13 col40\" ></td>\n",
       "      <td id=\"T_be83f_row13_col41\" class=\"data row13 col41\" ></td>\n",
       "      <td id=\"T_be83f_row13_col42\" class=\"data row13 col42\" ></td>\n",
       "      <td id=\"T_be83f_row13_col43\" class=\"data row13 col43\" ></td>\n",
       "      <td id=\"T_be83f_row13_col44\" class=\"data row13 col44\" ></td>\n",
       "      <td id=\"T_be83f_row13_col45\" class=\"data row13 col45\" ></td>\n",
       "      <td id=\"T_be83f_row13_col46\" class=\"data row13 col46\" ></td>\n",
       "      <td id=\"T_be83f_row13_col47\" class=\"data row13 col47\" ></td>\n",
       "      <td id=\"T_be83f_row13_col48\" class=\"data row13 col48\" ></td>\n",
       "      <td id=\"T_be83f_row13_col49\" class=\"data row13 col49\" ></td>\n",
       "      <td id=\"T_be83f_row13_col50\" class=\"data row13 col50\" ></td>\n",
       "      <td id=\"T_be83f_row13_col51\" class=\"data row13 col51\" ></td>\n",
       "      <td id=\"T_be83f_row13_col52\" class=\"data row13 col52\" ></td>\n",
       "      <td id=\"T_be83f_row13_col53\" class=\"data row13 col53\" ></td>\n",
       "      <td id=\"T_be83f_row13_col54\" class=\"data row13 col54\" ></td>\n",
       "      <td id=\"T_be83f_row13_col55\" class=\"data row13 col55\" ></td>\n",
       "      <td id=\"T_be83f_row13_col56\" class=\"data row13 col56\" >20</td>\n",
       "      <td id=\"T_be83f_row13_col57\" class=\"data row13 col57\" ></td>\n",
       "      <td id=\"T_be83f_row13_col58\" class=\"data row13 col58\" ></td>\n",
       "      <td id=\"T_be83f_row13_col59\" class=\"data row13 col59\" ></td>\n",
       "      <td id=\"T_be83f_row13_col60\" class=\"data row13 col60\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f51f40642e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ok = \"- OK -\"\n",
    "oom = \"- OOM -\"\n",
    "timeout = \"- TIMEOUT -\"\n",
    "error_mapping = {\n",
    "    \"TimeoutError\": timeout,\n",
    "    \"status code '137'\": oom,\n",
    "    \"MemoryError: Unable to allocate\": oom,\n",
    "    \"ValueError: Expected 2D array, got 1D array instead\": \"Wrong shape error\",\n",
    "    \"could not broadcast input array from shape\": \"Wrong shape error\",\n",
    "    \"not aligned\": \"Wrong shape error\",  # shapes (20,) and (19,500) not aligned\n",
    "    \"array must not contain infs or NaNs\": \"unexpected Inf or NaN\",\n",
    "    \"contains NaN\": \"unexpected Inf or NaN\",\n",
    "    \"cannot convert float NaN to integer\": \"unexpected Inf or NaN\",\n",
    "    \"Error(s) in loading state_dict\": \"Model loading error\",\n",
    "    \"EOFError\": \"Model loading error\",\n",
    "    \"Restoring from checkpoint failed\": \"Model loading error\",\n",
    "    \"RecursionError: maximum recursion depth exceeded in comparison\": \"Max recursion depth exceeded\",\n",
    "    \"but PCA is expecting\": \"BROKEN Exathlon DATASETS\",  # ValueError: X has 44 features, but PCA is expecting 43 features as input.\n",
    "    \"input.size(-1) must be equal to input_size\": \"BROKEN Exathlon DATASETS\",\n",
    "    \"ValueError: The condensed distance matrix must contain only finite values.\": \"LinAlgError\",\n",
    "    \"LinAlgError\": \"LinAlgError\",\n",
    "    \"NameError: name 'nan' is not defined\": \"Not converged\",\n",
    "    \"Could not form valid cluster separation\": \"Not converged\",\n",
    "    \"contamination must be in\": \"Invariance/assumption not met\",\n",
    "    \"Data must not be constant\": \"Invariance/assumption not met\",\n",
    "    \"Cannot compute initial seasonals using heuristic method with less than two full seasonal cycles in the data\": \"Invariance/assumption not met\",\n",
    "    \"ValueError: Anom detection needs at least 2 periods worth of data\": \"Invariance/assumption not met\",\n",
    "    \"`dataset` input should have multiple elements\": \"Invariance/assumption not met\",\n",
    "    \"Cannot take a larger sample than population\": \"Invariance/assumption not met\",\n",
    "    \"num_samples should be a positive integer value\": \"Invariance/assumption not met\",\n",
    "    \"Cannot use heuristic method to compute initial seasonal and levels with less than periods + 10 datapoints\": \"Invariance/assumption not met\",\n",
    "    \"ValueError: The window size must be less than or equal to 0\": \"Invariance/assumption not met\",\n",
    "    \"The window size must be less than or equal to\": \"Incompatible parameters\",\n",
    "    \"window_size has to be greater\": \"Incompatible parameters\",\n",
    "    \"Set a higher piecewise_median_period_weeks\": \"Incompatible parameters\",\n",
    "    \"OutOfBoundsDatetime: cannot convert input with unit 'm'\": \"Incompatible parameters\",\n",
    "    \"`window_size` must be at least 4\": \"Incompatible parameters\",\n",
    "    \"elements of 'k' must be between\": \"Incompatible parameters\",\n",
    "    \"Expected n_neighbors <= n_samples\": \"Incompatible parameters\",\n",
    "    \"PAA size can't be greater than the timeseries size\": \"Incompatible parameters\",\n",
    "    \"All window sizes must be greater than or equal to\": \"Incompatible parameters\",\n",
    "    \"ValueError: __len__() should return >= 0\": \"Bug\",\n",
    "    \"stack expects a non-empty TensorList\": \"Bug\",\n",
    "    \"expected non-empty vector\": \"Bug\",\n",
    "    \"Found array with 0 feature(s)\": \"Bug\",\n",
    "    \"ValueError: On entry to DLASCL parameter number 4 had an illegal value\": \"Bug\",\n",
    "    \"Sample larger than population or is negative\": \"Bug\",\n",
    "    \"ZeroDivisionError\": \"Bug\",\n",
    "    \"IndexError\": \"Bug\",\n",
    "    \"status code '139'\": \"Bug\",  # segfault\n",
    "    \"replacement has length zero\": \"Bug\",\n",
    "    \"missing value where TRUE/FALSE needed\": \"Bug\",\n",
    "    \"invalid subscript type 'list'\": \"Bug\",\n",
    "    \"subscript out of bounds\": \"Bug\",\n",
    "    \"invalid argument to unary operator\": \"Bug\",\n",
    "    \"negative length vectors are not allowed\": \"Bug\",\n",
    "    \"negative dimensions are not allowed\": \"Bug\",\n",
    "    \"`std` must be positive\": \"Bug\",\n",
    "    \"does not have key\": \"Bug\",  # State '1' does not have key '1'\n",
    "    \"Less than 2 uniques breaks left\": \"Bug\",\n",
    "    \"The encoder for value is invalid\": \"Bug\",\n",
    "    \"arange: cannot compute length\": \"Bug\",\n",
    "    \"n_components=3 must be between 0 and min(n_samples, n_features)\": \"Bug\",\n",
    "    \"must match the size of tensor b\": \"Wrong shape error\",\n",
    "}\n",
    "\n",
    "def get_folder(index):\n",
    "    series = df.loc[index]\n",
    "    if series[\"collection\"] == \"GutenTAG\":\n",
    "        result_path = gutentag_result_path\n",
    "        dataset_name = series[\"dataset\"] + \".\" + series[\"dataset_training_type\"].lower().replace(\"_\", \"-\")\n",
    "    else:\n",
    "        result_path = benchmark_result_path\n",
    "        dataset_name = series[\"dataset\"]\n",
    "    path = (\n",
    "        result_path /\n",
    "        series[\"algorithm\"] /\n",
    "        series[\"hyper_params_id\"] /\n",
    "        series[\"collection\"] /\n",
    "        dataset_name /\n",
    "        str(series[\"repetition\"])\n",
    "    )\n",
    "    return path\n",
    "    \n",
    "def category_from_logfile(logfile):\n",
    "    with logfile.open() as fh:\n",
    "        log = fh.read()\n",
    "    for error in error_mapping:\n",
    "        if error in log:\n",
    "            return error_mapping[error]\n",
    "    #print(log)\n",
    "    return \"other\"\n",
    "    \n",
    "def extract_category(series):\n",
    "    status = series[\"status\"]\n",
    "    msg = series[\"error_message\"]\n",
    "    if status == \"Status.OK\":\n",
    "        return ok\n",
    "    elif status == \"Status.TIMEOUT\":\n",
    "        return timeout\n",
    "    # status is ERROR:\n",
    "    elif \"DockerAlgorithmFailedError\" in msg:\n",
    "        path = get_folder(series.name) / \"execution.log\"\n",
    "        if path.exists():\n",
    "            return category_from_logfile(path)\n",
    "        return \"DockerAlgorithmFailedError\"\n",
    "    else:\n",
    "        m = re.search(\"^([\\w]+)\\(.*\\)\", msg)\n",
    "        if m:\n",
    "            error = m.group(1)\n",
    "        else:\n",
    "            error = msg\n",
    "        return f\"TimeEval:{error}\"\n",
    "\n",
    "df[\"error_category\"] = df.apply(extract_category, axis=\"columns\", raw=False)\n",
    "df_error_category_overview = df.pivot_table(index=\"error_category\", columns=\"algorithm\", values=\"repetition\", aggfunc=\"count\")\n",
    "df_error_category_overview.insert(0, \"ALL (sum)\", df_error_category_overview.sum(axis=1))\n",
    "\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_error_category_overview.style.format(\"{:.0f}\", na_rep=\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Error summary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_9aa15_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >count</th>\n",
       "      <th class=\"col_heading level0 col1\" >percentage</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >error_category</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_9aa15_level0_row0\" class=\"row_heading level0 row0\" >- OK -</th>\n",
       "      <td id=\"T_9aa15_row0_col0\" class=\"data row0 col0\" >82914</td>\n",
       "      <td id=\"T_9aa15_row0_col1\" class=\"data row0 col1\" >86.31%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9aa15_level0_row1\" class=\"row_heading level0 row1\" >- OOM -</th>\n",
       "      <td id=\"T_9aa15_row1_col0\" class=\"data row1 col0\" >2826</td>\n",
       "      <td id=\"T_9aa15_row1_col1\" class=\"data row1 col1\" >02.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9aa15_level0_row2\" class=\"row_heading level0 row2\" >- TIMEOUT -</th>\n",
       "      <td id=\"T_9aa15_row2_col0\" class=\"data row2 col0\" >5584</td>\n",
       "      <td id=\"T_9aa15_row2_col1\" class=\"data row2 col1\" >05.81%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_9aa15_level0_row3\" class=\"row_heading level0 row3\" >- ERROR -</th>\n",
       "      <td id=\"T_9aa15_row3_col0\" class=\"data row3 col0\" >4744</td>\n",
       "      <td id=\"T_9aa15_row3_col1\" class=\"data row3 col1\" >04.94%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f52206a93d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_error_summary = pd.DataFrame(df_error_category_overview.sum(axis=1))\n",
    "df_error_summary.columns = [\"count\"]\n",
    "\n",
    "df_error_summary.loc[\"- ERROR -\", \"count\"] = df_error_summary[~df_error_summary.index.str.startswith(\"- \")].sum().item()\n",
    "df_error_summary = df_error_summary.drop(df_error_summary[~df_error_summary.index.str.startswith(\"- \")].index)\n",
    "\n",
    "all_count = df_error_summary.sum().item()\n",
    "df_error_summary[\"percentage\"] = df_error_summary / all_count\n",
    "df_error_summary[\"count\"] = df_error_summary[\"count\"].astype(np.int_)\n",
    "df_error_summary.style.format({\"percentage\": \"{:06.2%}\".format})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add algorithm metadata (families and research areas):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>algo_display_name</th>\n",
       "      <th>algo_family</th>\n",
       "      <th>algo_area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>ARIMA</td>\n",
       "      <td>forecasting</td>\n",
       "      <td>Statistics (Regression &amp; Forecasting)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>ARIMA</td>\n",
       "      <td>forecasting</td>\n",
       "      <td>Statistics (Regression &amp; Forecasting)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>ARIMA</td>\n",
       "      <td>forecasting</td>\n",
       "      <td>Statistics (Regression &amp; Forecasting)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>ARIMA</td>\n",
       "      <td>forecasting</td>\n",
       "      <td>Statistics (Regression &amp; Forecasting)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARIMA</td>\n",
       "      <td>ARIMA</td>\n",
       "      <td>forecasting</td>\n",
       "      <td>Statistics (Regression &amp; Forecasting)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48029</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>VALMOD</td>\n",
       "      <td>distance</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48030</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>VALMOD</td>\n",
       "      <td>distance</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48031</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>VALMOD</td>\n",
       "      <td>distance</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48032</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>VALMOD</td>\n",
       "      <td>distance</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48033</th>\n",
       "      <td>VALMOD</td>\n",
       "      <td>VALMOD</td>\n",
       "      <td>distance</td>\n",
       "      <td>Data Mining</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48034 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      algorithm algo_display_name  algo_family  \\\n",
       "0         ARIMA             ARIMA  forecasting   \n",
       "1         ARIMA             ARIMA  forecasting   \n",
       "2         ARIMA             ARIMA  forecasting   \n",
       "3         ARIMA             ARIMA  forecasting   \n",
       "4         ARIMA             ARIMA  forecasting   \n",
       "...         ...               ...          ...   \n",
       "48029    VALMOD            VALMOD     distance   \n",
       "48030    VALMOD            VALMOD     distance   \n",
       "48031    VALMOD            VALMOD     distance   \n",
       "48032    VALMOD            VALMOD     distance   \n",
       "48033    VALMOD            VALMOD     distance   \n",
       "\n",
       "                                   algo_area  \n",
       "0      Statistics (Regression & Forecasting)  \n",
       "1      Statistics (Regression & Forecasting)  \n",
       "2      Statistics (Regression & Forecasting)  \n",
       "3      Statistics (Regression & Forecasting)  \n",
       "4      Statistics (Regression & Forecasting)  \n",
       "...                                      ...  \n",
       "48029                            Data Mining  \n",
       "48030                            Data Mining  \n",
       "48031                            Data Mining  \n",
       "48032                            Data Mining  \n",
       "48033                            Data Mining  \n",
       "\n",
       "[48034 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo_meta = {\n",
    "    \"AD-LTI\": {\"name\": \"AD-LTI\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"\", \"display_name\": \"AD-LTI\", \"tex_command\": \"\\\\adlti[cite]{}\"},\n",
    "    \"ARIMA\": {\"name\": \"ARIMA\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"forecasting\", \"image_name\": \"arima\", \"display_name\": \"ARIMA\", \"tex_command\": \"\\\\arima[cite]{}\"},\n",
    "    \"Autoencoder (AE)\": {\"name\": \"Autoencoder (AE)\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"autoencoder\", \"display_name\": \"AE\", \"tex_command\": \"\\\\autoencoder[cite]{}\"},\n",
    "    \"Bagel\": {\"name\": \"Bagel\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"bagel\", \"display_name\": \"Bagel\", \"tex_command\": \"\\\\bagel[cite]{}\"},\n",
    "    \"CBLOF\": {\"name\": \"CBLOF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distance\", \"image_name\": \"cblof\", \"display_name\": \"CBLOF\", \"tex_command\": \"\\\\cblof[cite]{}\"},\n",
    "    \"COF\": {\"name\": \"COF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distance\", \"image_name\": \"cof\", \"display_name\": \"COF\", \"tex_command\": \"\\\\cof[cite]{}\"},\n",
    "    \"COPOD\": {\"name\": \"COPOD\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distribution\", \"image_name\": \"copod\", \"display_name\": \"COPOD\", \"tex_command\": \"\\\\copod[cite]{}\"},\n",
    "    \"DenoisingAutoEncoder (DAE)\": {\"name\": \"DenoisingAutoEncoder (DAE)\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"dae\", \"display_name\": \"DAE\", \"tex_command\": \"\\\\dae[cite]{}\"},\n",
    "    \"DBStream\": {\"name\": \"DBStream\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distance\", \"image_name\": \"dbstream\", \"display_name\": \"DBStream\", \"tex_command\": \"\\\\dbstream[cite]{}\"},\n",
    "    \"DeepAnT\": {\"name\": \"DeepAnT\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"deepant\", \"display_name\": \"DeepAnT\", \"tex_command\": \"\\\\deepant[cite]{}\"},\n",
    "    \"DeepNAP\": {\"name\": \"DeepNAP\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"deepnap\", \"display_name\": \"DeepNAP\", \"tex_command\": \"\\\\deepnap[cite]{}\"},\n",
    "    \"Donut\": {\"name\": \"Donut\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"donut\", \"display_name\": \"Donut\", \"tex_command\": \"\\\\donut[cite]{}\"},\n",
    "    \"DSPOT\": {\"name\": \"DSPOT\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"distribution\", \"image_name\": \"dspot\", \"display_name\": \"DSPOT\", \"tex_command\": \"\\\\dspot[cite]{}\"},\n",
    "    \"DWT-MLEAD\": {\"name\": \"DWT-MLEAD\", \"research_area\": \"Signal Analysis\", \"method_family\": \"distribution\", \"image_name\": \"dwt_mlead\", \"display_name\": \"DWT-MLEAD\", \"tex_command\": \"\\\\dwtmlead[cite]{}\"},\n",
    "    \"Extended Isolation Forest (EIF)\": {\"name\": \"Extended Isolation Forest (EIF)\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"trees\", \"image_name\": \"eif\", \"display_name\": \"EIF\", \"tex_command\": \"\\\\eif[cite]{}\"},\n",
    "    \"EncDec-AD\": {\"name\": \"EncDec-AD\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"encdec_ad\", \"display_name\": \"EncDec-AD\", \"tex_command\": \"\\\\encdecad[cite]{}\"},\n",
    "    \"Ensemble GI\": {\"name\": \"Ensemble GI\", \"research_area\": \"Data Mining\", \"method_family\": \"encoding\", \"image_name\": \"ensemble_gi\", \"display_name\": \"Ensemble GI\", \"tex_command\": \"\\\\ensemblegi[cite]{}\"},\n",
    "    \"Fast-MCD\": {\"name\": \"Fast-MCD\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"distribution\", \"image_name\": \"fast_mcd\", \"display_name\": \"Fast-MCD\", \"tex_command\": \"\\\\fastmcd[cite]{}\"},\n",
    "    \"FFT\": {\"name\": \"FFT\", \"research_area\": \"Signal Analysis\", \"method_family\": \"reconstruction\", \"image_name\": \"fft\", \"display_name\": \"FFT\", \"tex_command\": \"\\\\fft[cite]{}\"},\n",
    "    \"Random Forest Regressor (RR)\": {\"name\": \"Random Forest Regressor (RR)\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"forecasting\", \"image_name\": \"generic_rf\", \"display_name\": \"RForest\", \"tex_command\": \"\\\\randomforest[cite]{}\"},\n",
    "    \"XGBoosting (RR)\": {\"name\": \"XGBoosting (RR)\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"forecasting\", \"image_name\": \"generic_xgb\", \"display_name\": \"XGBoosting\", \"tex_command\": \"\\\\xgboosting[cite]{}\"},\n",
    "    \"GrammarViz\": {\"name\": \"GrammarViz\", \"research_area\": \"Data Mining\", \"method_family\": \"encoding\", \"image_name\": \"grammarviz3\", \"display_name\": \"GrammarViz\", \"tex_command\": \"\\\\grammarviz[cite]{}\"},\n",
    "    \"HBOS\": {\"name\": \"HBOS\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"distance\", \"image_name\": \"hbos\", \"display_name\": \"HBOS\", \"tex_command\": \"\\\\hbos[cite]{}\"},\n",
    "    \"HealthESN\": {\"name\": \"HealthESN\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"health_esn\", \"display_name\": \"HealthESN\", \"tex_command\": \"\\\\healthesn[cite]{}\"},\n",
    "    \"Hybrid Isolation Forest (HIF)\": {\"name\": \"Hybrid Isolation Forest (HIF)\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"trees\", \"image_name\": \"hif\", \"display_name\": \"HIF\", \"tex_command\": \"\\\\hif[cite]{}\"},\n",
    "    \"HOT SAX\": {\"name\": \"HOT SAX\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"hotsax\", \"display_name\": \"HOT SAX\", \"tex_command\": \"\\\\hotsax[cite]{}\"},\n",
    "    \"Hybrid KNN\": {\"name\": \"Hybrid KNN\", \"research_area\": \"Deep Learning\", \"method_family\": \"distance\", \"image_name\": \"hybrid_knn\", \"display_name\": \"Hybrid KNN\", \"tex_command\": \"\\\\hybridknn[cite]{}\"},\n",
    "    \"IF-LOF\": {\"name\": \"IF-LOF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"trees\", \"image_name\": \"if_lof\", \"display_name\": \"IF-LOF\", \"tex_command\": \"\\\\iflof[cite]{}\"},\n",
    "    \"Isolation Forest (iForest)\": {\"name\": \"Isolation Forest (iForest)\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"trees\", \"image_name\": \"iforest\", \"display_name\": \"iForest\", \"tex_command\": \"\\\\iforest[cite]{}\"},\n",
    "    \"ImageEmbeddingCAE\": {\"name\": \"ImageEmbeddingCAE\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"img_embedding_cae\", \"display_name\": \"IE-CAE\", \"tex_command\": \"\\\\iecae[cite]{}\"},\n",
    "    \"k-Means\": {\"name\": \"k-Means\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"distance\", \"image_name\": \"kmeans\", \"display_name\": \"k-Means\", \"tex_command\": \"\\\\kmeans[cite]{}\"},\n",
    "    \"KNN\": {\"name\": \"KNN\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"distance\", \"image_name\": \"knn\", \"display_name\": \"KNN\", \"tex_command\": \"\\\\knn[cite]{}\"},\n",
    "    \"LaserDBN\": {\"name\": \"LaserDBN\", \"research_area\": \"Stochastic Learning\", \"method_family\": \"encoding\", \"image_name\": \"laser_dbn\", \"display_name\": \"LaserDBN\", \"tex_command\": \"\\\\laserdbn[cite]{}\"},\n",
    "    \"Left STAMPi\": {\"name\": \"Left STAMPi\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"left_stampi\", \"display_name\": \"Left STAMPi\", \"tex_command\": \"\\\\leftstampi[cite]{}\"},\n",
    "    \"LOF\": {\"name\": \"LOF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distance\", \"image_name\": \"lof\", \"display_name\": \"LOF\", \"tex_command\": \"\\\\lof[cite]{}\"},\n",
    "    \"LSTM-AD\": {\"name\": \"LSTM-AD\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"lstm_ad\", \"display_name\": \"LSTM-AD\", \"tex_command\": \"\\\\lstmad[cite]{}\"},\n",
    "    \"LSTM-VAE\": {\"name\": \"LSTM-VAE\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"lstm_vae\", \"display_name\": \"LSTM-VAE\", \"tex_command\": \"\\\\lstmvae[cite]{}\"},\n",
    "    \"MedianMethod\": {\"name\": \"MedianMethod\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"forecasting\", \"image_name\": \"median_method\", \"display_name\": \"MedianMethod\", \"tex_command\": \"\\\\medianmethod[cite]{}\"},\n",
    "    \"MSCRED\": {\"name\": \"MSCRED\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"mscred\", \"display_name\": \"MSCRED\", \"tex_command\": \"\\\\mscred[cite]{}\"},\n",
    "    \"MTAD-GAT\": {\"name\": \"MTAD-GAT\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"mtad_gat\", \"display_name\": \"MTAD-GAT\", \"tex_command\": \"\\\\mtadgat[cite]{}\"},\n",
    "    \"MultiHMM\": {\"name\": \"MultiHMM\", \"research_area\": \"Stochastic Learning\", \"method_family\": \"encoding\", \"image_name\": \"multi_hmm\", \"display_name\": \"MultiHMM\", \"tex_command\": \"\\\\multihmm[cite]{}\"},\n",
    "    \"NormA\": {\"name\": \"NormA\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"norma\", \"display_name\": \"NormA-SJ\", \"tex_command\": \"\\\\norma[cite]{}\"},\n",
    "    \"Normalizing Flows\": {\"name\": \"Normalizing Flows\", \"research_area\": \"Deep Learning\", \"method_family\": \"distribution\", \"image_name\": \"normalizing_flows\", \"display_name\": \"Normalizing Flows\", \"tex_command\": \"\\\\normalizingflows[cite]{}\"},\n",
    "    \"NoveltySVR\": {\"name\": \"NoveltySVR\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"forecasting\", \"image_name\": \"novelty_svr\", \"display_name\": \"NoveltySVR\", \"tex_command\": \"\\\\noveltysvr[cite]{}\"},\n",
    "    \"NumentaHTM\": {\"name\": \"NumentaHTM\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"numenta_htm\", \"display_name\": \"NumentaHTM\", \"tex_command\": \"\\\\numentahtm[cite]{}\"},\n",
    "    \"OceanWNN\": {\"name\": \"OceanWNN\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"ocean_wnn\", \"display_name\": \"OceanWNN\", \"tex_command\": \"\\\\oceanwnn[cite]{}\"},\n",
    "    \"OmniAnomaly\": {\"name\": \"OmniAnomaly\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"omnianomaly\", \"display_name\": \"OmniAnomaly\", \"tex_command\": \"\\\\omnianomaly[cite]{}\"},\n",
    "    \"PCC\": {\"name\": \"PCC\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"pcc\", \"display_name\": \"PCC\", \"tex_command\": \"\\\\pcc[cite]{}\"},\n",
    "    \"PCI\": {\"name\": \"PCI\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"reconstruction\", \"image_name\": \"pci\", \"display_name\": \"PCI\", \"tex_command\": \"\\\\pci[cite]{}\"},\n",
    "    \"PhaseSpace-SVM\": {\"name\": \"PhaseSpace-SVM\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"distance\", \"image_name\": \"phasespace_svm\", \"display_name\": \"PS-SVM\", \"tex_command\": \"\\\\phasespacesvm[cite]{}\"},\n",
    "    \"PST\": {\"name\": \"PST\", \"research_area\": \"Data Mining\", \"method_family\": \"trees\", \"image_name\": \"pst\", \"display_name\": \"PST\", \"tex_command\": \"\\\\pst[cite]{}\"},\n",
    "    \"Random Black Forest (RR)\": {\"name\": \"Random Black Forest (RR)\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"forecasting\", \"image_name\": \"random_black_forest\", \"display_name\": \"RBForest\", \"tex_command\": \"\\\\randomblackforest[cite]{}\"},\n",
    "    \"RobustPCA\": {\"name\": \"RobustPCA\", \"research_area\": \"Classic Machine Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"robust_pca\", \"display_name\": \"RobustPCA\", \"tex_command\": \"\\\\robustpca[cite]{}\"},\n",
    "    \"S-H-ESD (Twitter)\": {\"name\": \"S-H-ESD (Twitter)\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"distribution\", \"image_name\": \"s_h_esd\", \"display_name\": \"S-H-ESD\", \"tex_command\": \"\\\\shesd[cite]{}\"},\n",
    "    \"SAND\": {\"name\": \"SAND\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"sand\", \"display_name\": \"SAND\", \"tex_command\": \"\\\\sand[cite]{}\"},\n",
    "    \"SARIMA\": {\"name\": \"SARIMA\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"forecasting\", \"image_name\": \"sarima\", \"display_name\": \"SARIMA\", \"tex_command\": \"\\\\sarima[cite]{}\"},\n",
    "    \"Series2Graph\": {\"name\": \"Series2Graph\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"series2graph\", \"display_name\": \"Series2Graph\", \"tex_command\": \"\\\\seriestograph[cite]{}\"},\n",
    "    \"Spectral Residual (SR)\": {\"name\": \"Spectral Residual (SR)\", \"research_area\": \"Signal Analysis\", \"method_family\": \"reconstruction\", \"image_name\": \"sr\", \"display_name\": \"SR\", \"tex_command\": \"\\\\sr[cite]{}\"},\n",
    "    \"SR-CNN\": {\"name\": \"SR-CNN\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"sr_cnn\", \"display_name\": \"SR-CNN\", \"tex_command\": \"\\\\srcnn[cite]{}\"},\n",
    "    \"SSA\": {\"name\": \"SSA\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"ssa\", \"display_name\": \"SSA\", \"tex_command\": \"\\\\ssa[cite]{}\"},\n",
    "    \"STAMP\": {\"name\": \"STAMP\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"stamp\", \"display_name\": \"STAMP\", \"tex_command\": \"\\\\stamp[cite]{}\"},\n",
    "    \"STOMP\": {\"name\": \"STOMP\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"stomp\", \"display_name\": \"STOMP\", \"tex_command\": \"\\\\stomp[cite]{}\"},\n",
    "    \"Subsequence Fast-MCD\": {\"name\": \"Subsequence Fast-MCD\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"distribution\", \"image_name\": \"subsequence_fast_mcd\", \"display_name\": \"Sub-Fast-MCD\", \"tex_command\": \"\\\\subsequencefastmcd[cite]{}\"},\n",
    "    \"Subsequence IF\": {\"name\": \"Subsequence IF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"trees\", \"image_name\": \"subsequence_if\", \"display_name\": \"Sub-IF\", \"tex_command\": \"\\\\subsequenceif[cite]{}\"},\n",
    "    \"Subsequence LOF\": {\"name\": \"Subsequence LOF\", \"research_area\": \"Ourlier Detection\", \"method_family\": \"distance\", \"image_name\": \"subsequence_lof\", \"display_name\": \"Sub-LOF\", \"tex_command\": \"\\\\subsequencelof[cite]{}\"},\n",
    "    \"TAnoGAN\": {\"name\": \"TAnoGAN\", \"research_area\": \"Deep Learning\", \"method_family\": \"reconstruction\", \"image_name\": \"tanogan\", \"display_name\": \"TAnoGAN\", \"tex_command\": \"\\\\tanogan[cite]{}\"},\n",
    "    \"TARZAN\": {\"name\": \"TARZAN\", \"research_area\": \"Data Mining\", \"method_family\": \"encoding\", \"image_name\": \"tarzan\", \"display_name\": \"TARZAN\", \"tex_command\": \"\\\\tarzan[cite]{}\"},\n",
    "    \"Telemanom\": {\"name\": \"Telemanom\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"telemanom\", \"display_name\": \"Telemanom\", \"tex_command\": \"\\\\telemanom[cite]{}\"},\n",
    "    \"Torsk\": {\"name\": \"Torsk\", \"research_area\": \"Deep Learning\", \"method_family\": \"forecasting\", \"image_name\": \"torsk\", \"display_name\": \"Torsk\", \"tex_command\": \"\\\\torsk[cite]{}\"},\n",
    "    \"Triple ES (Holt-Winter's)\": {\"name\": \"Triple ES (Holt-Winter's)\", \"research_area\": \"Statistics (Regression & Forecasting)\", \"method_family\": \"forecasting\", \"image_name\": \"triple_es\", \"display_name\": \"Triple ES\", \"tex_command\": \"\\\\triplees[cite]{}\"},\n",
    "    \"TSBitmap\": {\"name\": \"TSBitmap\", \"research_area\": \"Data Mining\", \"method_family\": \"encoding\", \"image_name\": \"ts_bitmap\", \"display_name\": \"TSBitmap\", \"tex_command\": \"\\\\tsbitmap[cite]{}\"},\n",
    "    \"VALMOD\": {\"name\": \"VALMOD\", \"research_area\": \"Data Mining\", \"method_family\": \"distance\", \"image_name\": \"valmod\", \"display_name\": \"VALMOD\", \"tex_command\": \"\\\\valmod[cite]{}\"},\n",
    "    \"Random Baseline\": {\"name\": \"Random Baseline\", \"research_area\": \"\", \"method_family\": \"baseline\", \"image_name\": \"baseline_random\", \"display_name\": \"Random Baseline\", \"tex_command\": \"Random Baseline\"},\n",
    "    \"Normal Baseline\": {\"name\": \"Normal Baseline\", \"research_area\": \"\", \"method_family\": \"baseline\", \"image_name\": \"baseline_normal\", \"display_name\": \"Normal Baseline\", \"tex_command\": \"Normal Baseline\"},\n",
    "    \"Increasing Baseline\": {\"name\": \"Increasing Baseline\", \"research_area\": \"\", \"method_family\": \"baseline\", \"image_name\": \"baseline_increasing\", \"display_name\": \"Increasing  Baseline\", \"tex_command\": \"Increasing Baseline\"}\n",
    "}\n",
    "\n",
    "# fix some typos:\n",
    "algo_meta[\"TAnoGan\"] = algo_meta[\"TAnoGAN\"]\n",
    "algo_meta[\"Random\"] = algo_meta[\"Random Baseline\"]\n",
    "algo_meta[\"random\"] = algo_meta[\"Random Baseline\"]\n",
    "algo_meta[\"normal\"] = algo_meta[\"Normal Baseline\"]\n",
    "algo_meta[\"increasing\"] = algo_meta[\"Increasing Baseline\"]\n",
    "\n",
    "df[\"algo_family\"] = df[\"algorithm\"].apply(lambda algo: algo_meta[algo][\"method_family\"])\n",
    "df[\"algo_area\"] = df[\"algorithm\"].apply(lambda algo: algo_meta[algo][\"research_area\"])\n",
    "df[\"algo_display_name\"] = df[\"algorithm\"].apply(lambda algo: algo_meta[algo][\"display_name\"])\n",
    "\n",
    "df[[\"algorithm\", \"algo_display_name\", \"algo_family\", \"algo_area\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove broken datasets\n",
    "\n",
    "Filter out datasets, which cannot be processed by at least `threshold`% of algorithms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed the following 71 datasets:\n"
     ]
    },
    {
     "data": {
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       "      <th class=\"blank\" >&nbsp;</th>\n",
       "      <th class=\"index_name level0\" >status</th>\n",
       "      <th class=\"col_heading level0 col0\" >Successful algorithms</th>\n",
       "      <th class=\"col_heading level0 col1\" >errors</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >collection</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level0_row0\" class=\"row_heading level0 row0\" rowspan=\"2\">Exathlon</th>\n",
       "      <th id=\"T_e1413_level1_row0\" class=\"row_heading level1 row0\" >4_1_100000_61-29</th>\n",
       "      <td id=\"T_e1413_row0_col0\" class=\"data row0 col0\" >46%</td>\n",
       "      <td id=\"T_e1413_row0_col1\" class=\"data row0 col1\" >7x - OOM -, 5x - TIMEOUT -, 1x Wrong shape error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row1\" class=\"row_heading level1 row1\" >4_1_100000_61-30</th>\n",
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       "      <td id=\"T_e1413_row1_col1\" class=\"data row1 col1\" >7x - OOM -, 5x - TIMEOUT -, 1x Wrong shape error</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level0_row2\" class=\"row_heading level0 row2\" rowspan=\"48\">GHL</th>\n",
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       "      <td id=\"T_e1413_row2_col0\" class=\"data row2 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row2_col1\" class=\"data row2 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
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       "    <tr>\n",
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       "      <td id=\"T_e1413_row3_col0\" class=\"data row3 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row3_col1\" class=\"data row3 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
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       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row4\" class=\"row_heading level1 row4\" >03_Lev_fault_Temp_corr_seed_19_vars_23</th>\n",
       "      <td id=\"T_e1413_row4_col0\" class=\"data row4 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row4_col1\" class=\"data row4 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row5\" class=\"row_heading level1 row5\" >04_Lev_fault_Temp_corr_seed_23_vars_23</th>\n",
       "      <td id=\"T_e1413_row5_col0\" class=\"data row5 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row5_col1\" class=\"data row5 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row6\" class=\"row_heading level1 row6\" >05_Lev_fault_Temp_corr_seed_27_vars_23</th>\n",
       "      <td id=\"T_e1413_row6_col0\" class=\"data row6 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row6_col1\" class=\"data row6 col1\" >8x - OOM -, 6x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row7\" class=\"row_heading level1 row7\" >06_Lev_fault_Temp_corr_seed_29_vars_23</th>\n",
       "      <td id=\"T_e1413_row7_col0\" class=\"data row7 col0\" >38%</td>\n",
       "      <td id=\"T_e1413_row7_col1\" class=\"data row7 col1\" >8x - TIMEOUT -, 7x - OOM -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row8\" class=\"row_heading level1 row8\" >07_Lev_fault_Temp_corr_seed_31_vars_23</th>\n",
       "      <td id=\"T_e1413_row8_col0\" class=\"data row8 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row8_col1\" class=\"data row8 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row9\" class=\"row_heading level1 row9\" >08_Lev_fault_Temp_corr_seed_33_vars_23</th>\n",
       "      <td id=\"T_e1413_row9_col0\" class=\"data row9 col0\" >38%</td>\n",
       "      <td id=\"T_e1413_row9_col1\" class=\"data row9 col1\" >8x - TIMEOUT -, 7x - OOM -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row10\" class=\"row_heading level1 row10\" >09_Lev_fault_Temp_corr_seed_37_vars_23</th>\n",
       "      <td id=\"T_e1413_row10_col0\" class=\"data row10 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row10_col1\" class=\"data row10 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row11\" class=\"row_heading level1 row11\" >10_Lev_fault_Temp_corr_seed_39_vars_23</th>\n",
       "      <td id=\"T_e1413_row11_col0\" class=\"data row11 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row11_col1\" class=\"data row11 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row12\" class=\"row_heading level1 row12\" >11_Lev_fault_Temp_corr_seed_41_vars_23</th>\n",
       "      <td id=\"T_e1413_row12_col0\" class=\"data row12 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row12_col1\" class=\"data row12 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row13\" class=\"row_heading level1 row13\" >12_Lev_fault_Temp_corr_seed_43_vars_23</th>\n",
       "      <td id=\"T_e1413_row13_col0\" class=\"data row13 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row13_col1\" class=\"data row13 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row14\" class=\"row_heading level1 row14\" >13_Lev_fault_Temp_corr_seed_666_vars_23</th>\n",
       "      <td id=\"T_e1413_row14_col0\" class=\"data row14 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row14_col1\" class=\"data row14 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row15\" class=\"row_heading level1 row15\" >14_Lev_fault_Temp_corr_seed_47_vars_23</th>\n",
       "      <td id=\"T_e1413_row15_col0\" class=\"data row15 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row15_col1\" class=\"data row15 col1\" >8x - OOM -, 6x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row16\" class=\"row_heading level1 row16\" >15_Lev_fault_Temp_corr_seed_49_vars_23</th>\n",
       "      <td id=\"T_e1413_row16_col0\" class=\"data row16 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row16_col1\" class=\"data row16 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row17\" class=\"row_heading level1 row17\" >16_Lev_fault_Temp_corr_seed_53_vars_23</th>\n",
       "      <td id=\"T_e1413_row17_col0\" class=\"data row17 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row17_col1\" class=\"data row17 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row18\" class=\"row_heading level1 row18\" >17_Lev_fault_Temp_corr_seed_57_vars_23</th>\n",
       "      <td id=\"T_e1413_row18_col0\" class=\"data row18 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row18_col1\" class=\"data row18 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row19\" class=\"row_heading level1 row19\" >18_Lev_fault_Temp_corr_seed_59_vars_23</th>\n",
       "      <td id=\"T_e1413_row19_col0\" class=\"data row19 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row19_col1\" class=\"data row19 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row20\" class=\"row_heading level1 row20\" >19_Lev_fault_Temp_corr_seed_62_vars_23</th>\n",
       "      <td id=\"T_e1413_row20_col0\" class=\"data row20 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row20_col1\" class=\"data row20 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row21\" class=\"row_heading level1 row21\" >20_Lev_fault_Temp_corr_seed_67_vars_23</th>\n",
       "      <td id=\"T_e1413_row21_col0\" class=\"data row21 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row21_col1\" class=\"data row21 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row22\" class=\"row_heading level1 row22\" >21_Lev_fault_Temp_corr_seed_73_vars_23</th>\n",
       "      <td id=\"T_e1413_row22_col0\" class=\"data row22 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row22_col1\" class=\"data row22 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row23\" class=\"row_heading level1 row23\" >22_Lev_fault_Temp_corr_seed_777_vars_23</th>\n",
       "      <td id=\"T_e1413_row23_col0\" class=\"data row23 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row23_col1\" class=\"data row23 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row24\" class=\"row_heading level1 row24\" >23_Lev_fault_Temp_corr_seed_79_vars_23</th>\n",
       "      <td id=\"T_e1413_row24_col0\" class=\"data row24 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row24_col1\" class=\"data row24 col1\" >8x - OOM -, 6x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row25\" class=\"row_heading level1 row25\" >24_Lev_fault_Temp_corr_seed_83_vars_23</th>\n",
       "      <td id=\"T_e1413_row25_col0\" class=\"data row25 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row25_col1\" class=\"data row25 col1\" >8x - OOM -, 6x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row26\" class=\"row_heading level1 row26\" >25_Lev_corr_Temp_fault_seed_111_vars_23</th>\n",
       "      <td id=\"T_e1413_row26_col0\" class=\"data row26 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row26_col1\" class=\"data row26 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row27\" class=\"row_heading level1 row27\" >26_Lev_corr_Temp_fault_seed_113_vars_23</th>\n",
       "      <td id=\"T_e1413_row27_col0\" class=\"data row27 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row27_col1\" class=\"data row27 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row28\" class=\"row_heading level1 row28\" >27_Lev_corr_Temp_fault_seed_115_vars_23</th>\n",
       "      <td id=\"T_e1413_row28_col0\" class=\"data row28 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row28_col1\" class=\"data row28 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row29\" class=\"row_heading level1 row29\" >28_Lev_corr_Temp_fault_seed_119_vars_23</th>\n",
       "      <td id=\"T_e1413_row29_col0\" class=\"data row29 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row29_col1\" class=\"data row29 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row30\" class=\"row_heading level1 row30\" >29_Lev_corr_Temp_fault_seed_120_vars_23</th>\n",
       "      <td id=\"T_e1413_row30_col0\" class=\"data row30 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row30_col1\" class=\"data row30 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row31\" class=\"row_heading level1 row31\" >30_Lev_corr_Temp_fault_seed_130_vars_23</th>\n",
       "      <td id=\"T_e1413_row31_col0\" class=\"data row31 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row31_col1\" class=\"data row31 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row32\" class=\"row_heading level1 row32\" >31_Lev_corr_Temp_fault_seed_132_vars_23</th>\n",
       "      <td id=\"T_e1413_row32_col0\" class=\"data row32 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row32_col1\" class=\"data row32 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row33\" class=\"row_heading level1 row33\" >32_Lev_corr_Temp_fault_seed_137_vars_23</th>\n",
       "      <td id=\"T_e1413_row33_col0\" class=\"data row33 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row33_col1\" class=\"data row33 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row34\" class=\"row_heading level1 row34\" >33_Lev_corr_Temp_fault_seed_139_vars_23</th>\n",
       "      <td id=\"T_e1413_row34_col0\" class=\"data row34 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row34_col1\" class=\"data row34 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row35\" class=\"row_heading level1 row35\" >34_Lev_corr_Temp_fault_seed_151_vars_23</th>\n",
       "      <td id=\"T_e1413_row35_col0\" class=\"data row35 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row35_col1\" class=\"data row35 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row36\" class=\"row_heading level1 row36\" >35_Lev_corr_Temp_fault_seed_153_vars_23</th>\n",
       "      <td id=\"T_e1413_row36_col0\" class=\"data row36 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row36_col1\" class=\"data row36 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row37\" class=\"row_heading level1 row37\" >36_Lev_corr_Temp_fault_seed_159_vars_23</th>\n",
       "      <td id=\"T_e1413_row37_col0\" class=\"data row37 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row37_col1\" class=\"data row37 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row38\" class=\"row_heading level1 row38\" >37_Lev_corr_Temp_fault_seed_163_vars_23</th>\n",
       "      <td id=\"T_e1413_row38_col0\" class=\"data row38 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row38_col1\" class=\"data row38 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row39\" class=\"row_heading level1 row39\" >38_Lev_corr_Temp_fault_seed_166_vars_23</th>\n",
       "      <td id=\"T_e1413_row39_col0\" class=\"data row39 col0\" >38%</td>\n",
       "      <td id=\"T_e1413_row39_col1\" class=\"data row39 col1\" >8x - TIMEOUT -, 7x - OOM -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row40\" class=\"row_heading level1 row40\" >39_Lev_corr_Temp_fault_seed_170_vars_23</th>\n",
       "      <td id=\"T_e1413_row40_col0\" class=\"data row40 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row40_col1\" class=\"data row40 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row41\" class=\"row_heading level1 row41\" >40_Lev_corr_Temp_fault_seed_180_vars_23</th>\n",
       "      <td id=\"T_e1413_row41_col0\" class=\"data row41 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row41_col1\" class=\"data row41 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row42\" class=\"row_heading level1 row42\" >41_Lev_corr_Temp_fault_seed_181_vars_23</th>\n",
       "      <td id=\"T_e1413_row42_col0\" class=\"data row42 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row42_col1\" class=\"data row42 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row43\" class=\"row_heading level1 row43\" >42_Lev_corr_Temp_fault_seed_185_vars_23</th>\n",
       "      <td id=\"T_e1413_row43_col0\" class=\"data row43 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row43_col1\" class=\"data row43 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row44\" class=\"row_heading level1 row44\" >43_Lev_corr_Temp_fault_seed_189_vars_23</th>\n",
       "      <td id=\"T_e1413_row44_col0\" class=\"data row44 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row44_col1\" class=\"data row44 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row45\" class=\"row_heading level1 row45\" >44_Lev_corr_Temp_fault_seed_190_vars_23</th>\n",
       "      <td id=\"T_e1413_row45_col0\" class=\"data row45 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row45_col1\" class=\"data row45 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row46\" class=\"row_heading level1 row46\" >45_Lev_corr_Temp_fault_seed_193_vars_23</th>\n",
       "      <td id=\"T_e1413_row46_col0\" class=\"data row46 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row46_col1\" class=\"data row46 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row47\" class=\"row_heading level1 row47\" >46_Lev_corr_Temp_fault_seed_194_vars_23</th>\n",
       "      <td id=\"T_e1413_row47_col0\" class=\"data row47 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row47_col1\" class=\"data row47 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row48\" class=\"row_heading level1 row48\" >47_Lev_corr_Temp_fault_seed_197_vars_23</th>\n",
       "      <td id=\"T_e1413_row48_col0\" class=\"data row48 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row48_col1\" class=\"data row48 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row49\" class=\"row_heading level1 row49\" >48_Lev_corr_Temp_fault_seed_199_vars_23</th>\n",
       "      <td id=\"T_e1413_row49_col0\" class=\"data row49 col0\" >42%</td>\n",
       "      <td id=\"T_e1413_row49_col1\" class=\"data row49 col1\" >7x - OOM -, 7x - TIMEOUT -</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level0_row50\" class=\"row_heading level0 row50\" >KDD-TSAD</th>\n",
       "      <th id=\"T_e1413_level1_row50\" class=\"row_heading level1 row50\" >241_UCR_Anomaly_taichidbS0715Master</th>\n",
       "      <td id=\"T_e1413_row50_col0\" class=\"data row50 col0\" >49%</td>\n",
       "      <td id=\"T_e1413_row50_col1\" class=\"data row50 col1\" >15x - TIMEOUT -, 8x - OOM -, 2x Bug, 2x Invariance/assumption not met, 1x Incompatible parameters, 1x other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level0_row51\" class=\"row_heading level0 row51\" rowspan=\"20\">OPPORTUNITY</th>\n",
       "      <th id=\"T_e1413_level1_row51\" class=\"row_heading level1 row51\" >S1-ADL1</th>\n",
       "      <td id=\"T_e1413_row51_col0\" class=\"data row51 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row51_col1\" class=\"data row51 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row52\" class=\"row_heading level1 row52\" >S1-ADL2</th>\n",
       "      <td id=\"T_e1413_row52_col0\" class=\"data row52 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row52_col1\" class=\"data row52 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row53\" class=\"row_heading level1 row53\" >S1-ADL3</th>\n",
       "      <td id=\"T_e1413_row53_col0\" class=\"data row53 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row53_col1\" class=\"data row53 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row54\" class=\"row_heading level1 row54\" >S1-ADL4</th>\n",
       "      <td id=\"T_e1413_row54_col0\" class=\"data row54 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row54_col1\" class=\"data row54 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row55\" class=\"row_heading level1 row55\" >S1-ADL5</th>\n",
       "      <td id=\"T_e1413_row55_col0\" class=\"data row55 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row55_col1\" class=\"data row55 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row56\" class=\"row_heading level1 row56\" >S2-ADL1</th>\n",
       "      <td id=\"T_e1413_row56_col0\" class=\"data row56 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row56_col1\" class=\"data row56 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row57\" class=\"row_heading level1 row57\" >S2-ADL2</th>\n",
       "      <td id=\"T_e1413_row57_col0\" class=\"data row57 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row57_col1\" class=\"data row57 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row58\" class=\"row_heading level1 row58\" >S2-ADL3</th>\n",
       "      <td id=\"T_e1413_row58_col0\" class=\"data row58 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row58_col1\" class=\"data row58 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row59\" class=\"row_heading level1 row59\" >S2-ADL4</th>\n",
       "      <td id=\"T_e1413_row59_col0\" class=\"data row59 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row59_col1\" class=\"data row59 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row60\" class=\"row_heading level1 row60\" >S2-ADL5</th>\n",
       "      <td id=\"T_e1413_row60_col0\" class=\"data row60 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row60_col1\" class=\"data row60 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row61\" class=\"row_heading level1 row61\" >S3-ADL1</th>\n",
       "      <td id=\"T_e1413_row61_col0\" class=\"data row61 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row61_col1\" class=\"data row61 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row62\" class=\"row_heading level1 row62\" >S3-ADL2</th>\n",
       "      <td id=\"T_e1413_row62_col0\" class=\"data row62 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row62_col1\" class=\"data row62 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row63\" class=\"row_heading level1 row63\" >S3-ADL3</th>\n",
       "      <td id=\"T_e1413_row63_col0\" class=\"data row63 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row63_col1\" class=\"data row63 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row64\" class=\"row_heading level1 row64\" >S3-ADL4</th>\n",
       "      <td id=\"T_e1413_row64_col0\" class=\"data row64 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row64_col1\" class=\"data row64 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row65\" class=\"row_heading level1 row65\" >S3-ADL5</th>\n",
       "      <td id=\"T_e1413_row65_col0\" class=\"data row65 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row65_col1\" class=\"data row65 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row66\" class=\"row_heading level1 row66\" >S4-ADL1</th>\n",
       "      <td id=\"T_e1413_row66_col0\" class=\"data row66 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row66_col1\" class=\"data row66 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row67\" class=\"row_heading level1 row67\" >S4-ADL2</th>\n",
       "      <td id=\"T_e1413_row67_col0\" class=\"data row67 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row67_col1\" class=\"data row67 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row68\" class=\"row_heading level1 row68\" >S4-ADL3</th>\n",
       "      <td id=\"T_e1413_row68_col0\" class=\"data row68 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row68_col1\" class=\"data row68 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Incompatible parameters</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row69\" class=\"row_heading level1 row69\" >S4-ADL4</th>\n",
       "      <td id=\"T_e1413_row69_col0\" class=\"data row69 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row69_col1\" class=\"data row69 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_e1413_level1_row70\" class=\"row_heading level1 row70\" >S4-ADL5</th>\n",
       "      <td id=\"T_e1413_row70_col0\" class=\"data row70 col0\" >8%</td>\n",
       "      <td id=\"T_e1413_row70_col1\" class=\"data row70 col1\" >10x unexpected Inf or NaN, 1x - OOM -, 1x Bug</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f5146e95ee0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threshold = 0.5\n",
    "\n",
    "removed_by_collection = []\n",
    "\n",
    "index_columns = [\"collection\", \"dataset\"]\n",
    "df_dataset_error_counts = df.pivot_table(index=index_columns, columns=[\"status\"], values=\"repetition\", aggfunc=\"count\")\n",
    "df_dataset_error_counts = df_dataset_error_counts.fillna(value=0).astype(np.int64)\n",
    "df_dataset_error_counts[\"ALL\"] = df_dataset_error_counts[\"Status.ERROR\"] + df_dataset_error_counts[\"Status.OK\"] + df_dataset_error_counts[\"Status.TIMEOUT\"]\n",
    "df_dataset_error_counts[\"Successful algorithms\"] = df_dataset_error_counts[\"Status.OK\"] / df_dataset_error_counts[\"ALL\"]\n",
    "df_dataset_error_counts = df_dataset_error_counts.sort_values(by=[\"Status.ERROR\"], ascending=False)\n",
    "\n",
    "df_dataset_to_remove = df_dataset_error_counts.loc[df_dataset_error_counts[\"Successful algorithms\"] < threshold, [\"Successful algorithms\"]]\n",
    "def find_error_categories(series):\n",
    "    collection, dataset = series.name\n",
    "    errors = df.loc[\n",
    "        (df[\"collection\"] == collection) &\n",
    "        (df[\"dataset\"] == dataset) &\n",
    "        (df[\"status\"] != \"Status.OK\"),\n",
    "        \"error_category\"\n",
    "    ].values\n",
    "    unique, counts = np.unique(errors, return_counts=True)\n",
    "    return \", \".join(f\"{c}x {n}\" for n, c in sorted(zip(unique, counts), key=lambda x: x[1], reverse=True))\n",
    "\n",
    "df_dataset_to_remove[\"errors\"] = df_dataset_to_remove.apply(find_error_categories, axis=\"columns\", raw=False)\n",
    "df_dataset_to_remove = df_dataset_to_remove.sort_index()\n",
    "\n",
    "df = df[~(\n",
    "    (df[\"collection\"].isin(df_dataset_to_remove.index.get_level_values(0))) & \n",
    "    (df[\"dataset\"].isin(df_dataset_to_remove.index.get_level_values(1)))\n",
    ")]\n",
    "\n",
    "for col, s in df_dataset_to_remove.reset_index().groupby(by=\"collection\")[[\"dataset\"]].count().iterrows():\n",
    "    removed_by_collection.append((col, s[\"dataset\"]))\n",
    "print(f\"Removed the following {len(df_dataset_to_remove)} datasets:\")\n",
    "display(df_dataset_to_remove.style.format({\"Successful algorithms\": \"{:2.0%}\"}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out datasets, for which no algorithm could achieve at least an ROC_AUC score of `threshold`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed the following 78 datasets:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Daphnet</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exathlon</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GutenTAG</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITDB</th>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAB</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormA</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMD</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVDB</th>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WebscopeS5</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            dataset\n",
       "collection         \n",
       "Daphnet           7\n",
       "Exathlon          2\n",
       "GutenTAG          6\n",
       "MITDB            25\n",
       "Metro             1\n",
       "NAB               2\n",
       "NormA             1\n",
       "SMD               2\n",
       "SVDB             30\n",
       "WebscopeS5        2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = 0.8\n",
    "\n",
    "df_dataset_to_remove = df.groupby(by=[\"collection\", \"dataset\"])[[\"ROC_AUC\"]].max()\n",
    "df_dataset_to_remove.columns = [\"Max ROC_AUC\"]\n",
    "df_dataset_to_remove = df_dataset_to_remove[df_dataset_to_remove[\"Max ROC_AUC\"] < 0.8]\n",
    "df_dataset_to_remove = df_dataset_to_remove.sort_index()\n",
    "\n",
    "df = df[~(\n",
    "    (df[\"collection\"].isin(df_dataset_to_remove.index.get_level_values(0))) & \n",
    "    (df[\"dataset\"].isin(df_dataset_to_remove.index.get_level_values(1)))\n",
    ")]\n",
    "for col, s in df_dataset_to_remove.reset_index().groupby(by=\"collection\")[[\"dataset\"]].count().iterrows():\n",
    "    removed_by_collection.append((col, s[\"dataset\"]))\n",
    "print(f\"Removed the following {len(df_dataset_to_remove)} datasets:\")\n",
    "df_dataset_to_remove.reset_index().groupby(by=\"collection\")[[\"dataset\"]].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Store preprocessed result dataframe to disk!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(result_root_path / \"paper-plots-results.csv\", index=False, header=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset overview (grouped by collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Origin</th>\n",
       "      <th>Dim.</th>\n",
       "      <th>Learn.</th>\n",
       "      <th># Datasets (total)</th>\n",
       "      <th># Datasets (selected)</th>\n",
       "      <th># Datasets (used)</th>\n",
       "      <th># Datasets (removed)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CalIt2</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Daphnet</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dodgers</th>\n",
       "      <td>real</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exathlon</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised/semi-supervised/supervised</td>\n",
       "      <td>39</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GHL</th>\n",
       "      <td>synthetic</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Genesis</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GutenTAG</th>\n",
       "      <td>synthetic</td>\n",
       "      <td>multivariate/univariate</td>\n",
       "      <td>unsupervised/supervised/semi-supervised</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>187</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IOPS</th>\n",
       "      <td>real</td>\n",
       "      <td>univariate</td>\n",
       "      <td>supervised</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KDD-TSAD</th>\n",
       "      <td>synthetic</td>\n",
       "      <td>univariate</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>249</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kitsune</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LTDB</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MGAB</th>\n",
       "      <td>synthetic</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MITDB</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metro</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NAB</th>\n",
       "      <td>real/synthetic</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASA-MSL</th>\n",
       "      <td>real</td>\n",
       "      <td>univariate</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>27</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NASA-SMAP</th>\n",
       "      <td>real</td>\n",
       "      <td>univariate</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>54</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NormA</th>\n",
       "      <td>real/synthetic</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OPPORTUNITY</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>24</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Occupancy</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>supervised</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SMD</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>semi-supervised</td>\n",
       "      <td>28</td>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SSA</th>\n",
       "      <td>real</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SVDB</th>\n",
       "      <td>real</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>78</td>\n",
       "      <td>46</td>\n",
       "      <td>16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WebscopeS5</th>\n",
       "      <td>real/synthetic</td>\n",
       "      <td>univariate</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>367</td>\n",
       "      <td>362</td>\n",
       "      <td>360</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Origin                     Dim.  \\\n",
       "CalIt2                 real             multivariate   \n",
       "Daphnet                real             multivariate   \n",
       "Dodgers                real               univariate   \n",
       "Exathlon               real             multivariate   \n",
       "GHL               synthetic             multivariate   \n",
       "Genesis                real             multivariate   \n",
       "GutenTAG          synthetic  multivariate/univariate   \n",
       "IOPS                   real               univariate   \n",
       "KDD-TSAD          synthetic               univariate   \n",
       "Kitsune                real             multivariate   \n",
       "LTDB                   real             multivariate   \n",
       "MGAB              synthetic               univariate   \n",
       "MITDB                  real             multivariate   \n",
       "Metro                  real             multivariate   \n",
       "NAB          real/synthetic               univariate   \n",
       "NASA-MSL               real               univariate   \n",
       "NASA-SMAP              real               univariate   \n",
       "NormA        real/synthetic               univariate   \n",
       "OPPORTUNITY            real             multivariate   \n",
       "Occupancy              real             multivariate   \n",
       "SMD                    real             multivariate   \n",
       "SSA                    real               univariate   \n",
       "SVDB                   real             multivariate   \n",
       "WebscopeS5   real/synthetic               univariate   \n",
       "\n",
       "                                              Learn.  # Datasets (total)  \\\n",
       "CalIt2                                  unsupervised                   1   \n",
       "Daphnet                                 unsupervised                  35   \n",
       "Dodgers                                 unsupervised                   1   \n",
       "Exathlon     unsupervised/semi-supervised/supervised                  39   \n",
       "GHL                                  semi-supervised                  48   \n",
       "Genesis                                 unsupervised                   1   \n",
       "GutenTAG     unsupervised/supervised/semi-supervised                 193   \n",
       "IOPS                                      supervised                  29   \n",
       "KDD-TSAD                             semi-supervised                 250   \n",
       "Kitsune                                 unsupervised                   9   \n",
       "LTDB                                    unsupervised                   7   \n",
       "MGAB                                    unsupervised                  10   \n",
       "MITDB                                   unsupervised                  48   \n",
       "Metro                                   unsupervised                   1   \n",
       "NAB                                     unsupervised                  58   \n",
       "NASA-MSL                             semi-supervised                  27   \n",
       "NASA-SMAP                            semi-supervised                  54   \n",
       "NormA                                   unsupervised                  21   \n",
       "OPPORTUNITY                             unsupervised                  24   \n",
       "Occupancy                                 supervised                   2   \n",
       "SMD                                  semi-supervised                  28   \n",
       "SSA                                     unsupervised                  23   \n",
       "SVDB                                    unsupervised                  78   \n",
       "WebscopeS5                              unsupervised                 367   \n",
       "\n",
       "             # Datasets (selected)  # Datasets (used)  # Datasets (removed)  \n",
       "CalIt2                           1                  1                     0  \n",
       "Daphnet                         10                  3                     7  \n",
       "Dodgers                          0                  0                     0  \n",
       "Exathlon                         4                  2                     4  \n",
       "GHL                             48                  0                    48  \n",
       "Genesis                          1                  1                     0  \n",
       "GutenTAG                       193                187                     6  \n",
       "IOPS                             4                  4                     0  \n",
       "KDD-TSAD                       250                249                     1  \n",
       "Kitsune                          0                  0                     0  \n",
       "LTDB                             0                  0                     0  \n",
       "MGAB                            10                 10                     0  \n",
       "MITDB                           29                  4                    25  \n",
       "Metro                            1                  0                     1  \n",
       "NAB                             58                 56                     2  \n",
       "NASA-MSL                        16                 16                     0  \n",
       "NASA-SMAP                       35                 35                     0  \n",
       "NormA                           10                  9                     1  \n",
       "OPPORTUNITY                     20                  0                    20  \n",
       "Occupancy                        0                  0                     0  \n",
       "SMD                             25                 23                     2  \n",
       "SSA                              0                  0                     0  \n",
       "SVDB                            46                 16                    30  \n",
       "WebscopeS5                     362                360                     2  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header = [\"Origin\", \"Dim.\", \"Learn.\", \"# Datasets (total)\", \"# Datasets (used)\"]\n",
    "df_data = pd.read_csv(result_root_path / \"..\" / \"data\" / \"benchmark-data\" / \"data-processed\" / \"datasets.csv\")\n",
    "df_data = df_data.drop(df_data.index[df_data[\"collection_name\"].isin(ignore_collections)])\n",
    "\n",
    "def combine(s):\n",
    "    return \"/\".join(s.unique())\n",
    "\n",
    "df_data = df_data\\\n",
    "    .groupby(by=\"collection_name\")[[\"dataset_type\", \"input_type\", \"train_type\", \"dataset_name\"]]\\\n",
    "    .agg({\"dataset_type\": combine, \"input_type\": combine, \"train_type\": combine, \"dataset_name\": \"count\"})\n",
    "df_data.columns = header[:-1]\n",
    "df_data.index.name = \"Collection Name\"\n",
    "\n",
    "# also add GutenTAG dataset collection\n",
    "df_tmp = pd.read_csv(result_root_path / \"..\" / \"data\" / \"test-cases\" / \"datasets.csv\")\n",
    "df_tmp[\"dataset_name\"] = df_tmp[\"dataset_name\"].str.split(\".\").str[0]\n",
    "df_tmp = df_tmp[[\"collection_name\", \"dataset_name\", \"dataset_type\", \"input_type\", \"train_type\"]]\\\n",
    "    .groupby(by=[\"collection_name\", \"dataset_name\"])\\\n",
    "    .agg({\"dataset_type\": combine, \"input_type\": combine, \"train_type\": combine})\\\n",
    "    .reset_index()\\\n",
    "    .groupby(by=[\"collection_name\"])\\\n",
    "    .agg({\"dataset_type\": combine, \"input_type\": combine, \"train_type\": combine, \"dataset_name\": \"count\"})\n",
    "df_tmp.columns = header[:-1]\n",
    "df_data = pd.concat([df_data, df_tmp])\n",
    "\n",
    "# get number of actually used datasets\n",
    "df_tmp = df[[\"collection\", \"dataset\"]].drop_duplicates().groupby(by=[\"collection\"]).count()\n",
    "df_tmp.columns = header[-1:]\n",
    "df_data = pd.merge(df_data, df_tmp, left_index=True, right_index=True, how=\"left\")\n",
    "\n",
    "# add number of removed datasetes\n",
    "df_tmp = pd.DataFrame(removed_by_collection, columns=[\"Collection Name\", \"# Datasets (removed)\"])\n",
    "df_tmp = df_tmp.groupby(by=\"Collection Name\").sum()\n",
    "df_data = pd.merge(df_data, df_tmp, left_index=True, right_index=True, how=\"left\")\n",
    "\n",
    "# add dataset selection (passed max contamination and min anomaly filters)\n",
    "df_data.loc[\"CalIt2\", \"# Datasets (selected)\"] = 1\n",
    "df_data.loc[\"Daphnet\", \"# Datasets (selected)\"] = 10\n",
    "df_data.loc[\"Exathlon\", \"# Datasets (selected)\"] = 4\n",
    "df_data.loc[\"GHL\", \"# Datasets (selected)\"] = 48\n",
    "df_data.loc[\"Genesis\", \"# Datasets (selected)\"] = 1\n",
    "df_data.loc[\"GutenTAG\", \"# Datasets (selected)\"] = df_data.loc[\"GutenTAG\", \"# Datasets (total)\"]\n",
    "df_data.loc[\"IOPS\", \"# Datasets (selected)\"] = 4\n",
    "df_data.loc[\"KDD-TSAD\", \"# Datasets (selected)\"] = 250\n",
    "#df_data.loc[\"Keogh\", \"# Datasets (selected)\"] = 3\n",
    "df_data.loc[\"MGAB\", \"# Datasets (selected)\"] = 10\n",
    "df_data.loc[\"MITDB\", \"# Datasets (selected)\"] = 29\n",
    "df_data.loc[\"Metro\", \"# Datasets (selected)\"] = 1\n",
    "df_data.loc[\"NAB\", \"# Datasets (selected)\"] = 58\n",
    "df_data.loc[\"NASA-MSL\", \"# Datasets (selected)\"] = 16\n",
    "df_data.loc[\"NASA-SMAP\", \"# Datasets (selected)\"] = 35\n",
    "df_data.loc[\"OPPORTUNITY\", \"# Datasets (selected)\"] = 20\n",
    "df_data.loc[\"SMD\", \"# Datasets (selected)\"] = 25\n",
    "df_data.loc[\"SVDB\", \"# Datasets (selected)\"] = 46\n",
    "df_data.loc[\"WebscopeS5\", \"# Datasets (selected)\"] = 362\n",
    "df_data.loc[\"NormA\", \"# Datasets (selected)\"] = 10\n",
    "\n",
    "df_data = df_data.sort_index().fillna(0)\n",
    "df_data[[\"# Datasets (total)\", \"# Datasets (selected)\", \"# Datasets (used)\", \"# Datasets (removed)\"]] = df_data[[\"# Datasets (total)\", \"# Datasets (selected)\", \"# Datasets (used)\", \"# Datasets (removed)\"]].astype(np.int_)\n",
    "df_data[[\"Origin\", \"Dim.\", \"Learn.\", \"# Datasets (total)\", \"# Datasets (selected)\", \"# Datasets (used)\", \"# Datasets (removed)\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality overview (boxplots)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sebastian.schmidl/.conda/envs/timeeval/lib/python3.8/site-packages/pandas/core/generic.py:4150: PerformanceWarning: dropping on a non-lexsorted multi-index without a level parameter may impact performance.\n",
      "  obj = obj._drop_axis(labels, axis, level=level, errors=errors)\n"
     ]
    }
   ],
   "source": [
    "dominant_aggregation = \"mean\"\n",
    "index_columns = [\"algo_input_dimensionality\", \"algo_training_type\", \"algorithm\"]\n",
    "\n",
    "df_asl = df.pivot(index=index_columns, columns=[\"collection\", \"dataset\"], values=\"ROC_AUC\")\n",
    "df_asl = df_asl.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl[dominant_aggregation] = df_asl.agg(dominant_aggregation, axis=1)\n",
    "df_asl = df_asl.reset_index().sort_values(by=index_columns[:-1] + [dominant_aggregation], ascending=True).set_index(index_columns)\n",
    "df_asl = df_asl.drop(columns=dominant_aggregation)\n",
    "#df_asl.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asl_pr = df.pivot(index=index_columns, columns=[\"collection\", \"dataset\"], values=\"PR_AUC\")\n",
    "df_asl_pr = df_asl_pr.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl_pr = df_asl_pr.reindex(df_asl.index)\n",
    "#df_asl_pr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asl_rangepr = df.pivot(index=index_columns, columns=[\"collection\", \"dataset\"], values=\"RANGE_PR_AUC\")\n",
    "df_asl_rangepr = df_asl_rangepr.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl_rangepr = df_asl_rangepr.reindex(df_asl.index)\n",
    "#df_asl_pr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_asl_gt = df[df[\"collection\"] == \"GutenTAG\"].pivot(index=index_columns, columns=[\"collection\", \"dataset\"], values=\"ROC_AUC\")\n",
    "df_asl_gt = df_asl_gt.dropna(axis=0, how=\"all\").dropna(axis=1, how=\"all\")\n",
    "df_asl_gt = df_asl_gt.reindex(df_asl.index)\n",
    "#df_asl_gt.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_4e95b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >dimensionality</th>\n",
       "      <th class=\"col_heading level0 col1\" >learning type</th>\n",
       "      <th class=\"col_heading level0 col2\" >algorithm</th>\n",
       "      <th class=\"col_heading level0 col3\" ># TIMEOUT</th>\n",
       "      <th class=\"col_heading level0 col4\" ># OOM</th>\n",
       "      <th class=\"col_heading level0 col5\" ># ERROR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_4e95b_row0_col0\" class=\"data row0 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row0_col1\" class=\"data row0 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row0_col2\" class=\"data row0 col2\" >Hybrid KNN</td>\n",
       "      <td id=\"T_4e95b_row0_col3\" class=\"data row0 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row0_col4\" class=\"data row0 col4\" >01%</td>\n",
       "      <td id=\"T_4e95b_row0_col5\" class=\"data row0 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_4e95b_row1_col0\" class=\"data row1 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row1_col1\" class=\"data row1 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row1_col2\" class=\"data row1 col2\" >TAnoGAN</td>\n",
       "      <td id=\"T_4e95b_row1_col3\" class=\"data row1 col3\" >65%</td>\n",
       "      <td id=\"T_4e95b_row1_col4\" class=\"data row1 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row1_col5\" class=\"data row1 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_4e95b_row2_col0\" class=\"data row2 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row2_col1\" class=\"data row2 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row2_col2\" class=\"data row2 col2\" >RobustPCA</td>\n",
       "      <td id=\"T_4e95b_row2_col3\" class=\"data row2 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row2_col4\" class=\"data row2 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row2_col5\" class=\"data row2 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "      <td id=\"T_4e95b_row3_col0\" class=\"data row3 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row3_col1\" class=\"data row3 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row3_col2\" class=\"data row3 col2\" >LaserDBN</td>\n",
       "      <td id=\"T_4e95b_row3_col3\" class=\"data row3 col3\" >04%</td>\n",
       "      <td id=\"T_4e95b_row3_col4\" class=\"data row3 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row3_col5\" class=\"data row3 col5\" >07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "      <td id=\"T_4e95b_row4_col0\" class=\"data row4 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row4_col1\" class=\"data row4 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row4_col2\" class=\"data row4 col2\" >OmniAnomaly</td>\n",
       "      <td id=\"T_4e95b_row4_col3\" class=\"data row4 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row4_col4\" class=\"data row4 col4\" >04%</td>\n",
       "      <td id=\"T_4e95b_row4_col5\" class=\"data row4 col5\" >02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row5\" class=\"row_heading level0 row5\" >5</th>\n",
       "      <td id=\"T_4e95b_row5_col0\" class=\"data row5 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row5_col1\" class=\"data row5 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row5_col2\" class=\"data row5 col2\" >DeepAnT</td>\n",
       "      <td id=\"T_4e95b_row5_col3\" class=\"data row5 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row5_col4\" class=\"data row5 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row5_col5\" class=\"data row5 col5\" >05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row6\" class=\"row_heading level0 row6\" >6</th>\n",
       "      <td id=\"T_4e95b_row6_col0\" class=\"data row6 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row6_col1\" class=\"data row6 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row6_col2\" class=\"data row6 col2\" >EncDec-AD</td>\n",
       "      <td id=\"T_4e95b_row6_col3\" class=\"data row6 col3\" >55%</td>\n",
       "      <td id=\"T_4e95b_row6_col4\" class=\"data row6 col4\" >26%</td>\n",
       "      <td id=\"T_4e95b_row6_col5\" class=\"data row6 col5\" >03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row7\" class=\"row_heading level0 row7\" >7</th>\n",
       "      <td id=\"T_4e95b_row7_col0\" class=\"data row7 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row7_col1\" class=\"data row7 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row7_col2\" class=\"data row7 col2\" >RBForest</td>\n",
       "      <td id=\"T_4e95b_row7_col3\" class=\"data row7 col3\" >06%</td>\n",
       "      <td id=\"T_4e95b_row7_col4\" class=\"data row7 col4\" >05%</td>\n",
       "      <td id=\"T_4e95b_row7_col5\" class=\"data row7 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row8\" class=\"row_heading level0 row8\" >8</th>\n",
       "      <td id=\"T_4e95b_row8_col0\" class=\"data row8 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row8_col1\" class=\"data row8 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row8_col2\" class=\"data row8 col2\" >Telemanom</td>\n",
       "      <td id=\"T_4e95b_row8_col3\" class=\"data row8 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row8_col4\" class=\"data row8 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row8_col5\" class=\"data row8 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row9\" class=\"row_heading level0 row9\" >9</th>\n",
       "      <td id=\"T_4e95b_row9_col0\" class=\"data row9 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row9_col1\" class=\"data row9 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row9_col2\" class=\"data row9 col2\" >HealthESN</td>\n",
       "      <td id=\"T_4e95b_row9_col3\" class=\"data row9 col3\" >66%</td>\n",
       "      <td id=\"T_4e95b_row9_col4\" class=\"data row9 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row9_col5\" class=\"data row9 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row10\" class=\"row_heading level0 row10\" >10</th>\n",
       "      <td id=\"T_4e95b_row10_col0\" class=\"data row10 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row10_col1\" class=\"data row10 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row10_col2\" class=\"data row10 col2\" >LSTM-AD</td>\n",
       "      <td id=\"T_4e95b_row10_col3\" class=\"data row10 col3\" >14%</td>\n",
       "      <td id=\"T_4e95b_row10_col4\" class=\"data row10 col4\" >50%</td>\n",
       "      <td id=\"T_4e95b_row10_col5\" class=\"data row10 col5\" >03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row11\" class=\"row_heading level0 row11\" >11</th>\n",
       "      <td id=\"T_4e95b_row11_col0\" class=\"data row11 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row11_col1\" class=\"data row11 col1\" >SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row11_col2\" class=\"data row11 col2\" >MultiHMM</td>\n",
       "      <td id=\"T_4e95b_row11_col3\" class=\"data row11 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row11_col4\" class=\"data row11 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row11_col5\" class=\"data row11 col5\" >52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row12\" class=\"row_heading level0 row12\" >12</th>\n",
       "      <td id=\"T_4e95b_row12_col0\" class=\"data row12 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row12_col1\" class=\"data row12 col1\" >SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row12_col2\" class=\"data row12 col2\" >HIF</td>\n",
       "      <td id=\"T_4e95b_row12_col3\" class=\"data row12 col3\" >01%</td>\n",
       "      <td id=\"T_4e95b_row12_col4\" class=\"data row12 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row12_col5\" class=\"data row12 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row13\" class=\"row_heading level0 row13\" >13</th>\n",
       "      <td id=\"T_4e95b_row13_col0\" class=\"data row13 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row13_col1\" class=\"data row13 col1\" >SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row13_col2\" class=\"data row13 col2\" >Normalizing Flows</td>\n",
       "      <td id=\"T_4e95b_row13_col3\" class=\"data row13 col3\" >60%</td>\n",
       "      <td id=\"T_4e95b_row13_col4\" class=\"data row13 col4\" >06%</td>\n",
       "      <td id=\"T_4e95b_row13_col5\" class=\"data row13 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row14\" class=\"row_heading level0 row14\" >14</th>\n",
       "      <td id=\"T_4e95b_row14_col0\" class=\"data row14 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row14_col1\" class=\"data row14 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row14_col2\" class=\"data row14 col2\" >PCC</td>\n",
       "      <td id=\"T_4e95b_row14_col3\" class=\"data row14 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row14_col4\" class=\"data row14 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row14_col5\" class=\"data row14 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row15\" class=\"row_heading level0 row15\" >15</th>\n",
       "      <td id=\"T_4e95b_row15_col0\" class=\"data row15 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row15_col1\" class=\"data row15 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row15_col2\" class=\"data row15 col2\" >COF</td>\n",
       "      <td id=\"T_4e95b_row15_col3\" class=\"data row15 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row15_col4\" class=\"data row15 col4\" >24%</td>\n",
       "      <td id=\"T_4e95b_row15_col5\" class=\"data row15 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row16\" class=\"row_heading level0 row16\" >16</th>\n",
       "      <td id=\"T_4e95b_row16_col0\" class=\"data row16 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row16_col1\" class=\"data row16 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row16_col2\" class=\"data row16 col2\" >LOF</td>\n",
       "      <td id=\"T_4e95b_row16_col3\" class=\"data row16 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row16_col4\" class=\"data row16 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row16_col5\" class=\"data row16 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row17\" class=\"row_heading level0 row17\" >17</th>\n",
       "      <td id=\"T_4e95b_row17_col0\" class=\"data row17 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row17_col1\" class=\"data row17 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row17_col2\" class=\"data row17 col2\" >IF-LOF</td>\n",
       "      <td id=\"T_4e95b_row17_col3\" class=\"data row17 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row17_col4\" class=\"data row17 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row17_col5\" class=\"data row17 col5\" >03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row18\" class=\"row_heading level0 row18\" >18</th>\n",
       "      <td id=\"T_4e95b_row18_col0\" class=\"data row18 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row18_col1\" class=\"data row18 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row18_col2\" class=\"data row18 col2\" >COPOD</td>\n",
       "      <td id=\"T_4e95b_row18_col3\" class=\"data row18 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row18_col4\" class=\"data row18 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row18_col5\" class=\"data row18 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row19\" class=\"row_heading level0 row19\" >19</th>\n",
       "      <td id=\"T_4e95b_row19_col0\" class=\"data row19 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row19_col1\" class=\"data row19 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row19_col2\" class=\"data row19 col2\" >CBLOF</td>\n",
       "      <td id=\"T_4e95b_row19_col3\" class=\"data row19 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row19_col4\" class=\"data row19 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row19_col5\" class=\"data row19 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row20\" class=\"row_heading level0 row20\" >20</th>\n",
       "      <td id=\"T_4e95b_row20_col0\" class=\"data row20 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row20_col1\" class=\"data row20 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row20_col2\" class=\"data row20 col2\" >DBStream</td>\n",
       "      <td id=\"T_4e95b_row20_col3\" class=\"data row20 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row20_col4\" class=\"data row20 col4\" >02%</td>\n",
       "      <td id=\"T_4e95b_row20_col5\" class=\"data row20 col5\" >78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row21\" class=\"row_heading level0 row21\" >21</th>\n",
       "      <td id=\"T_4e95b_row21_col0\" class=\"data row21 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row21_col1\" class=\"data row21 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row21_col2\" class=\"data row21 col2\" >HBOS</td>\n",
       "      <td id=\"T_4e95b_row21_col3\" class=\"data row21 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row21_col4\" class=\"data row21 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row21_col5\" class=\"data row21 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row22\" class=\"row_heading level0 row22\" >22</th>\n",
       "      <td id=\"T_4e95b_row22_col0\" class=\"data row22 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row22_col1\" class=\"data row22 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row22_col2\" class=\"data row22 col2\" >iForest</td>\n",
       "      <td id=\"T_4e95b_row22_col3\" class=\"data row22 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row22_col4\" class=\"data row22 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row22_col5\" class=\"data row22 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row23\" class=\"row_heading level0 row23\" >23</th>\n",
       "      <td id=\"T_4e95b_row23_col0\" class=\"data row23 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row23_col1\" class=\"data row23 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row23_col2\" class=\"data row23 col2\" >EIF</td>\n",
       "      <td id=\"T_4e95b_row23_col3\" class=\"data row23 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row23_col4\" class=\"data row23 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row23_col5\" class=\"data row23 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row24\" class=\"row_heading level0 row24\" >24</th>\n",
       "      <td id=\"T_4e95b_row24_col0\" class=\"data row24 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row24_col1\" class=\"data row24 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row24_col2\" class=\"data row24 col2\" >Torsk</td>\n",
       "      <td id=\"T_4e95b_row24_col3\" class=\"data row24 col3\" >07%</td>\n",
       "      <td id=\"T_4e95b_row24_col4\" class=\"data row24 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row24_col5\" class=\"data row24 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row25\" class=\"row_heading level0 row25\" >25</th>\n",
       "      <td id=\"T_4e95b_row25_col0\" class=\"data row25 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row25_col1\" class=\"data row25 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row25_col2\" class=\"data row25 col2\" >KNN</td>\n",
       "      <td id=\"T_4e95b_row25_col3\" class=\"data row25 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row25_col4\" class=\"data row25 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row25_col5\" class=\"data row25 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row26\" class=\"row_heading level0 row26\" >26</th>\n",
       "      <td id=\"T_4e95b_row26_col0\" class=\"data row26 col0\" >MULTIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row26_col1\" class=\"data row26 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row26_col2\" class=\"data row26 col2\" >k-Means</td>\n",
       "      <td id=\"T_4e95b_row26_col3\" class=\"data row26 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row26_col4\" class=\"data row26 col4\" >04%</td>\n",
       "      <td id=\"T_4e95b_row26_col5\" class=\"data row26 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row27\" class=\"row_heading level0 row27\" >27</th>\n",
       "      <td id=\"T_4e95b_row27_col0\" class=\"data row27 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row27_col1\" class=\"data row27 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row27_col2\" class=\"data row27 col2\" >TARZAN</td>\n",
       "      <td id=\"T_4e95b_row27_col3\" class=\"data row27 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row27_col4\" class=\"data row27 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row27_col5\" class=\"data row27 col5\" >18%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row28\" class=\"row_heading level0 row28\" >28</th>\n",
       "      <td id=\"T_4e95b_row28_col0\" class=\"data row28 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row28_col1\" class=\"data row28 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row28_col2\" class=\"data row28 col2\" >SR-CNN</td>\n",
       "      <td id=\"T_4e95b_row28_col3\" class=\"data row28 col3\" >22%</td>\n",
       "      <td id=\"T_4e95b_row28_col4\" class=\"data row28 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row28_col5\" class=\"data row28 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row29\" class=\"row_heading level0 row29\" >29</th>\n",
       "      <td id=\"T_4e95b_row29_col0\" class=\"data row29 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row29_col1\" class=\"data row29 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row29_col2\" class=\"data row29 col2\" >Bagel</td>\n",
       "      <td id=\"T_4e95b_row29_col3\" class=\"data row29 col3\" >19%</td>\n",
       "      <td id=\"T_4e95b_row29_col4\" class=\"data row29 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row29_col5\" class=\"data row29 col5\" >02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row30\" class=\"row_heading level0 row30\" >30</th>\n",
       "      <td id=\"T_4e95b_row30_col0\" class=\"data row30 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row30_col1\" class=\"data row30 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row30_col2\" class=\"data row30 col2\" >OceanWNN</td>\n",
       "      <td id=\"T_4e95b_row30_col3\" class=\"data row30 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row30_col4\" class=\"data row30 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row30_col5\" class=\"data row30 col5\" >10%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row31\" class=\"row_heading level0 row31\" >31</th>\n",
       "      <td id=\"T_4e95b_row31_col0\" class=\"data row31 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row31_col1\" class=\"data row31 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row31_col2\" class=\"data row31 col2\" >XGBoosting</td>\n",
       "      <td id=\"T_4e95b_row31_col3\" class=\"data row31 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row31_col4\" class=\"data row31 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row31_col5\" class=\"data row31 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row32\" class=\"row_heading level0 row32\" >32</th>\n",
       "      <td id=\"T_4e95b_row32_col0\" class=\"data row32 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row32_col1\" class=\"data row32 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row32_col2\" class=\"data row32 col2\" >IE-CAE</td>\n",
       "      <td id=\"T_4e95b_row32_col3\" class=\"data row32 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row32_col4\" class=\"data row32 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row32_col5\" class=\"data row32 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row33\" class=\"row_heading level0 row33\" >33</th>\n",
       "      <td id=\"T_4e95b_row33_col0\" class=\"data row33 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row33_col1\" class=\"data row33 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row33_col2\" class=\"data row33 col2\" >RForest</td>\n",
       "      <td id=\"T_4e95b_row33_col3\" class=\"data row33 col3\" >12%</td>\n",
       "      <td id=\"T_4e95b_row33_col4\" class=\"data row33 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row33_col5\" class=\"data row33 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row34\" class=\"row_heading level0 row34\" >34</th>\n",
       "      <td id=\"T_4e95b_row34_col0\" class=\"data row34 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row34_col1\" class=\"data row34 col1\" >SEMI_SUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row34_col2\" class=\"data row34 col2\" >Donut</td>\n",
       "      <td id=\"T_4e95b_row34_col3\" class=\"data row34 col3\" >01%</td>\n",
       "      <td id=\"T_4e95b_row34_col4\" class=\"data row34 col4\" >01%</td>\n",
       "      <td id=\"T_4e95b_row34_col5\" class=\"data row34 col5\" >02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row35\" class=\"row_heading level0 row35\" >35</th>\n",
       "      <td id=\"T_4e95b_row35_col0\" class=\"data row35 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row35_col1\" class=\"data row35 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row35_col2\" class=\"data row35 col2\" >S-H-ESD</td>\n",
       "      <td id=\"T_4e95b_row35_col3\" class=\"data row35 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row35_col4\" class=\"data row35 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row35_col5\" class=\"data row35 col5\" >49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row36\" class=\"row_heading level0 row36\" >36</th>\n",
       "      <td id=\"T_4e95b_row36_col0\" class=\"data row36 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row36_col1\" class=\"data row36 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row36_col2\" class=\"data row36 col2\" >FFT</td>\n",
       "      <td id=\"T_4e95b_row36_col3\" class=\"data row36 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row36_col4\" class=\"data row36 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row36_col5\" class=\"data row36 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row37\" class=\"row_heading level0 row37\" >37</th>\n",
       "      <td id=\"T_4e95b_row37_col0\" class=\"data row37 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row37_col1\" class=\"data row37 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row37_col2\" class=\"data row37 col2\" >DSPOT</td>\n",
       "      <td id=\"T_4e95b_row37_col3\" class=\"data row37 col3\" >06%</td>\n",
       "      <td id=\"T_4e95b_row37_col4\" class=\"data row37 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row37_col5\" class=\"data row37 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row38\" class=\"row_heading level0 row38\" >38</th>\n",
       "      <td id=\"T_4e95b_row38_col0\" class=\"data row38 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row38_col1\" class=\"data row38 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row38_col2\" class=\"data row38 col2\" >TSBitmap</td>\n",
       "      <td id=\"T_4e95b_row38_col3\" class=\"data row38 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row38_col4\" class=\"data row38 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row38_col5\" class=\"data row38 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row39\" class=\"row_heading level0 row39\" >39</th>\n",
       "      <td id=\"T_4e95b_row39_col0\" class=\"data row39 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row39_col1\" class=\"data row39 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row39_col2\" class=\"data row39 col2\" >HOT SAX</td>\n",
       "      <td id=\"T_4e95b_row39_col3\" class=\"data row39 col3\" >24%</td>\n",
       "      <td id=\"T_4e95b_row39_col4\" class=\"data row39 col4\" >01%</td>\n",
       "      <td id=\"T_4e95b_row39_col5\" class=\"data row39 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row40\" class=\"row_heading level0 row40\" >40</th>\n",
       "      <td id=\"T_4e95b_row40_col0\" class=\"data row40 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row40_col1\" class=\"data row40 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row40_col2\" class=\"data row40 col2\" >SSA</td>\n",
       "      <td id=\"T_4e95b_row40_col3\" class=\"data row40 col3\" >01%</td>\n",
       "      <td id=\"T_4e95b_row40_col4\" class=\"data row40 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row40_col5\" class=\"data row40 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row41\" class=\"row_heading level0 row41\" >41</th>\n",
       "      <td id=\"T_4e95b_row41_col0\" class=\"data row41 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row41_col1\" class=\"data row41 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row41_col2\" class=\"data row41 col2\" >PST</td>\n",
       "      <td id=\"T_4e95b_row41_col3\" class=\"data row41 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row41_col4\" class=\"data row41 col4\" >04%</td>\n",
       "      <td id=\"T_4e95b_row41_col5\" class=\"data row41 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row42\" class=\"row_heading level0 row42\" >42</th>\n",
       "      <td id=\"T_4e95b_row42_col0\" class=\"data row42 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row42_col1\" class=\"data row42 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row42_col2\" class=\"data row42 col2\" >PS-SVM</td>\n",
       "      <td id=\"T_4e95b_row42_col3\" class=\"data row42 col3\" >12%</td>\n",
       "      <td id=\"T_4e95b_row42_col4\" class=\"data row42 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row42_col5\" class=\"data row42 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row43\" class=\"row_heading level0 row43\" >43</th>\n",
       "      <td id=\"T_4e95b_row43_col0\" class=\"data row43 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row43_col1\" class=\"data row43 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row43_col2\" class=\"data row43 col2\" >SR</td>\n",
       "      <td id=\"T_4e95b_row43_col3\" class=\"data row43 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row43_col4\" class=\"data row43 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row43_col5\" class=\"data row43 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row44\" class=\"row_heading level0 row44\" >44</th>\n",
       "      <td id=\"T_4e95b_row44_col0\" class=\"data row44 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row44_col1\" class=\"data row44 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row44_col2\" class=\"data row44 col2\" >MedianMethod</td>\n",
       "      <td id=\"T_4e95b_row44_col3\" class=\"data row44 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row44_col4\" class=\"data row44 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row44_col5\" class=\"data row44 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row45\" class=\"row_heading level0 row45\" >45</th>\n",
       "      <td id=\"T_4e95b_row45_col0\" class=\"data row45 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row45_col1\" class=\"data row45 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row45_col2\" class=\"data row45 col2\" >Sub-IF</td>\n",
       "      <td id=\"T_4e95b_row45_col3\" class=\"data row45 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row45_col4\" class=\"data row45 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row45_col5\" class=\"data row45 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row46\" class=\"row_heading level0 row46\" >46</th>\n",
       "      <td id=\"T_4e95b_row46_col0\" class=\"data row46 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row46_col1\" class=\"data row46 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row46_col2\" class=\"data row46 col2\" >NormA-SJ</td>\n",
       "      <td id=\"T_4e95b_row46_col3\" class=\"data row46 col3\" >10%</td>\n",
       "      <td id=\"T_4e95b_row46_col4\" class=\"data row46 col4\" >01%</td>\n",
       "      <td id=\"T_4e95b_row46_col5\" class=\"data row46 col5\" >03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row47\" class=\"row_heading level0 row47\" >47</th>\n",
       "      <td id=\"T_4e95b_row47_col0\" class=\"data row47 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row47_col1\" class=\"data row47 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row47_col2\" class=\"data row47 col2\" >NumentaHTM</td>\n",
       "      <td id=\"T_4e95b_row47_col3\" class=\"data row47 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row47_col4\" class=\"data row47 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row47_col5\" class=\"data row47 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row48\" class=\"row_heading level0 row48\" >48</th>\n",
       "      <td id=\"T_4e95b_row48_col0\" class=\"data row48 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row48_col1\" class=\"data row48 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row48_col2\" class=\"data row48 col2\" >Triple ES</td>\n",
       "      <td id=\"T_4e95b_row48_col3\" class=\"data row48 col3\" >15%</td>\n",
       "      <td id=\"T_4e95b_row48_col4\" class=\"data row48 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row48_col5\" class=\"data row48 col5\" >09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row49\" class=\"row_heading level0 row49\" >49</th>\n",
       "      <td id=\"T_4e95b_row49_col0\" class=\"data row49 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row49_col1\" class=\"data row49 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row49_col2\" class=\"data row49 col2\" >STAMP</td>\n",
       "      <td id=\"T_4e95b_row49_col3\" class=\"data row49 col3\" >04%</td>\n",
       "      <td id=\"T_4e95b_row49_col4\" class=\"data row49 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row49_col5\" class=\"data row49 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row50\" class=\"row_heading level0 row50\" >50</th>\n",
       "      <td id=\"T_4e95b_row50_col0\" class=\"data row50 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row50_col1\" class=\"data row50 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row50_col2\" class=\"data row50 col2\" >STOMP</td>\n",
       "      <td id=\"T_4e95b_row50_col3\" class=\"data row50 col3\" >02%</td>\n",
       "      <td id=\"T_4e95b_row50_col4\" class=\"data row50 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row50_col5\" class=\"data row50 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row51\" class=\"row_heading level0 row51\" >51</th>\n",
       "      <td id=\"T_4e95b_row51_col0\" class=\"data row51 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row51_col1\" class=\"data row51 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row51_col2\" class=\"data row51 col2\" >PCI</td>\n",
       "      <td id=\"T_4e95b_row51_col3\" class=\"data row51 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row51_col4\" class=\"data row51 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row51_col5\" class=\"data row51 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row52\" class=\"row_heading level0 row52\" >52</th>\n",
       "      <td id=\"T_4e95b_row52_col0\" class=\"data row52 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row52_col1\" class=\"data row52 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row52_col2\" class=\"data row52 col2\" >ARIMA</td>\n",
       "      <td id=\"T_4e95b_row52_col3\" class=\"data row52 col3\" >07%</td>\n",
       "      <td id=\"T_4e95b_row52_col4\" class=\"data row52 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row52_col5\" class=\"data row52 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row53\" class=\"row_heading level0 row53\" >53</th>\n",
       "      <td id=\"T_4e95b_row53_col0\" class=\"data row53 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row53_col1\" class=\"data row53 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row53_col2\" class=\"data row53 col2\" >Series2Graph</td>\n",
       "      <td id=\"T_4e95b_row53_col3\" class=\"data row53 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row53_col4\" class=\"data row53 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row53_col5\" class=\"data row53 col5\" >05%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row54\" class=\"row_heading level0 row54\" >54</th>\n",
       "      <td id=\"T_4e95b_row54_col0\" class=\"data row54 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row54_col1\" class=\"data row54 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row54_col2\" class=\"data row54 col2\" >Left STAMPi</td>\n",
       "      <td id=\"T_4e95b_row54_col3\" class=\"data row54 col3\" >02%</td>\n",
       "      <td id=\"T_4e95b_row54_col4\" class=\"data row54 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row54_col5\" class=\"data row54 col5\" >01%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row55\" class=\"row_heading level0 row55\" >55</th>\n",
       "      <td id=\"T_4e95b_row55_col0\" class=\"data row55 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row55_col1\" class=\"data row55 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row55_col2\" class=\"data row55 col2\" >SAND</td>\n",
       "      <td id=\"T_4e95b_row55_col3\" class=\"data row55 col3\" >05%</td>\n",
       "      <td id=\"T_4e95b_row55_col4\" class=\"data row55 col4\" >01%</td>\n",
       "      <td id=\"T_4e95b_row55_col5\" class=\"data row55 col5\" >22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row56\" class=\"row_heading level0 row56\" >56</th>\n",
       "      <td id=\"T_4e95b_row56_col0\" class=\"data row56 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row56_col1\" class=\"data row56 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row56_col2\" class=\"data row56 col2\" >VALMOD</td>\n",
       "      <td id=\"T_4e95b_row56_col3\" class=\"data row56 col3\" >01%</td>\n",
       "      <td id=\"T_4e95b_row56_col4\" class=\"data row56 col4\" >09%</td>\n",
       "      <td id=\"T_4e95b_row56_col5\" class=\"data row56 col5\" >11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row57\" class=\"row_heading level0 row57\" >57</th>\n",
       "      <td id=\"T_4e95b_row57_col0\" class=\"data row57 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row57_col1\" class=\"data row57 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row57_col2\" class=\"data row57 col2\" >DWT-MLEAD</td>\n",
       "      <td id=\"T_4e95b_row57_col3\" class=\"data row57 col3\" >00%</td>\n",
       "      <td id=\"T_4e95b_row57_col4\" class=\"data row57 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row57_col5\" class=\"data row57 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row58\" class=\"row_heading level0 row58\" >58</th>\n",
       "      <td id=\"T_4e95b_row58_col0\" class=\"data row58 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row58_col1\" class=\"data row58 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row58_col2\" class=\"data row58 col2\" >GrammarViz</td>\n",
       "      <td id=\"T_4e95b_row58_col3\" class=\"data row58 col3\" >03%</td>\n",
       "      <td id=\"T_4e95b_row58_col4\" class=\"data row58 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row58_col5\" class=\"data row58 col5\" >00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4e95b_level0_row59\" class=\"row_heading level0 row59\" >59</th>\n",
       "      <td id=\"T_4e95b_row59_col0\" class=\"data row59 col0\" >UNIVARIATE</td>\n",
       "      <td id=\"T_4e95b_row59_col1\" class=\"data row59 col1\" >UNSUPERVISED</td>\n",
       "      <td id=\"T_4e95b_row59_col2\" class=\"data row59 col2\" >Sub-LOF</td>\n",
       "      <td id=\"T_4e95b_row59_col3\" class=\"data row59 col3\" >02%</td>\n",
       "      <td id=\"T_4e95b_row59_col4\" class=\"data row59 col4\" >00%</td>\n",
       "      <td id=\"T_4e95b_row59_col5\" class=\"data row59 col5\" >00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f514bfa6970>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "relative = True\n",
    "\n",
    "df_algorithm_error_counts = df.pivot_table(index=[\"algorithm\"], columns=[\"error_category\"], values=\"repetition\", aggfunc=\"count\")\n",
    "df_algorithm_error_counts = df_algorithm_error_counts.fillna(value=0).astype(np.int64)\n",
    "error_categories = [c for c in df_algorithm_error_counts.columns if not c.startswith(\"-\")]\n",
    "df_algorithm_error_counts[\"- ERROR -\"] = df_algorithm_error_counts[error_categories].sum(axis=1)\n",
    "df_algorithm_error_counts = df_algorithm_error_counts.drop(columns=error_categories)\n",
    "df_algorithm_error_counts[\"- ALL -\"] = df_algorithm_error_counts.sum(axis=1)\n",
    "df_algorithm_error_counts.columns = [c.split(\" \")[1] for c in df_algorithm_error_counts.columns]\n",
    "\n",
    "def get_error_count(algo, tpe=\"ERROR\"):\n",
    "    if relative:\n",
    "        return df_algorithm_error_counts.loc[algo, tpe] / df_algorithm_error_counts.loc[algo, \"ALL\"]\n",
    "    else:\n",
    "        return df_algorithm_error_counts.loc[algo, tpe]\n",
    "    \n",
    "overview = []\n",
    "for d, l, a in df_asl.index:\n",
    "    overview.append([d,l,a])\n",
    "df_overview_table = pd.DataFrame(overview, columns=[\"dimensionality\", \"learning type\", \"algorithm\"])\n",
    "df_overview_table[\"# TIMEOUT\"] = df_overview_table[\"algorithm\"].apply(get_error_count, tpe=\"TIMEOUT\")\n",
    "df_overview_table[\"# OOM\"] = df_overview_table[\"algorithm\"].apply(get_error_count, tpe=\"OOM\")\n",
    "df_overview_table[\"# ERROR\"] = df_overview_table[\"algorithm\"].apply(get_error_count, tpe=\"ERROR\")\n",
    "df_overview_table[\"algorithm\"] = df_overview_table[\"algorithm\"].apply(lambda algo: algo_meta[algo][\"display_name\"])\n",
    "\n",
    "percent_format = \"{:03.0%}\"\n",
    "with pd.option_context(\"display.max_rows\", None, \"display.max_columns\", None):\n",
    "    display(df_overview_table.style.format({\"# TIMEOUT\": percent_format, \"# OOM\": percent_format, \"# ERROR\": percent_format}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fliers=False\n",
    "show_labels=False\n",
    "title = \"Overall algorithm quality\"\n",
    "df_boxplot = df_asl.T\n",
    "\n",
    "labels = df_boxplot.columns\n",
    "labels = [f\"{c[2]}\" for c in labels]\n",
    "\n",
    "fig, axs = plt.subplots(1, 4, figsize=(20, 20), sharey=True)\n",
    "ax = axs[0]\n",
    "ax.boxplot([df_boxplot[c].dropna().values for c in df_boxplot.columns], labels=labels, sym=None if fliers else \"\", vert=False, meanline=True, showmeans=True, showfliers=fliers, manage_ticks=True)\n",
    "ax.set_xlabel(\"ROC_AUC\")\n",
    "ax.set_title(\"ROC_AUC all datasets\")\n",
    "ax.set_xlim(-0.05, 1.05)\n",
    "if not show_labels:\n",
    "    ax.tick_params(axis=\"y\", which=\"both\", left=True, right=False, labelleft=False, labelright=False)\n",
    "\n",
    "ax = axs[1]\n",
    "ax.boxplot([df_asl_pr.T[c].dropna().values for c in df_boxplot.columns], labels=labels, sym=None if fliers else \"\", vert=False, meanline=True, showmeans=True, showfliers=fliers, manage_ticks=True)\n",
    "ax.set_xlabel(\"PR_AUC\")\n",
    "ax.set_title(\"PR_AUC all datasets\")\n",
    "ax.set_xlim(-0.05, 1.05)\n",
    "ax.tick_params(axis=\"y\", which=\"both\", left=False, right=False, labelleft=False, labelright=False)\n",
    "\n",
    "ax = axs[2]\n",
    "ax.boxplot([df_asl_rangepr.T[c].dropna().values for c in df_boxplot.columns], labels=labels, sym=None if fliers else \"\", vert=False, meanline=True, showmeans=True, showfliers=fliers, manage_ticks=True)\n",
    "ax.set_xlabel(\"RANGE_PR_AUC\")\n",
    "ax.set_title(\"RANGE_PR_AUC all datasets\")\n",
    "ax.set_xlim(-0.05, 1.05)\n",
    "ax.tick_params(axis=\"y\", which=\"both\", left=False, right=False, labelleft=False, labelright=False)\n",
    "\n",
    "ax = axs[3]\n",
    "ax.boxplot([df_asl_gt.T[c].dropna().values for c in df_boxplot.columns], labels=labels, sym=None if fliers else \"\", vert=False, meanline=True, showmeans=True, showfliers=fliers, manage_ticks=True)\n",
    "ax.set_xlabel(\"ROC_AUC\")\n",
    "#ax.xaxis.set_label_position(\"top\")\n",
    "ax.set_title(\"ROC_AUC GutenTAG datasets only\")\n",
    "ax.set_xlim(-0.05, 1.05)\n",
    "ax.tick_params(axis=\"y\", which=\"both\", left=False, right=False, labelleft=False, labelright=False)\n",
    "\n",
    "# ax = axs[3]\n",
    "# ax.axis(\"tight\")\n",
    "# ax.set_axis_off()\n",
    "# table = ax.table(cellText=df_boxplot.columns, loc=\"center\", edges=\"open\", fontsize=200)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare ranking between all datasets and GutenTAG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_6809b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >ROC_AUC_gt</th>\n",
       "      <th class=\"col_heading level0 col1\" >Rank_gt</th>\n",
       "      <th class=\"col_heading level0 col2\" >ROC_AUC_all</th>\n",
       "      <th class=\"col_heading level0 col3\" >Rank_all</th>\n",
       "      <th class=\"col_heading level0 col4\" >Diff_rank</th>\n",
       "      <th class=\"col_heading level0 col5\" >Diff ROC_AUC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row0\" class=\"row_heading level0 row0\" >Torsk</th>\n",
       "      <td id=\"T_6809b_row0_col0\" class=\"data row0 col0\" >0.885825</td>\n",
       "      <td id=\"T_6809b_row0_col1\" class=\"data row0 col1\" >8.000000</td>\n",
       "      <td id=\"T_6809b_row0_col2\" class=\"data row0 col2\" >0.682120</td>\n",
       "      <td id=\"T_6809b_row0_col3\" class=\"data row0 col3\" >28.000000</td>\n",
       "      <td id=\"T_6809b_row0_col4\" class=\"data row0 col4\" >+20</td>\n",
       "      <td id=\"T_6809b_row0_col5\" class=\"data row0 col5\" >-0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row1\" class=\"row_heading level0 row1\" >SSA</th>\n",
       "      <td id=\"T_6809b_row1_col0\" class=\"data row1 col0\" >0.771233</td>\n",
       "      <td id=\"T_6809b_row1_col1\" class=\"data row1 col1\" >26.000000</td>\n",
       "      <td id=\"T_6809b_row1_col2\" class=\"data row1 col2\" >0.596694</td>\n",
       "      <td id=\"T_6809b_row1_col3\" class=\"data row1 col3\" >46.000000</td>\n",
       "      <td id=\"T_6809b_row1_col4\" class=\"data row1 col4\" >+20</td>\n",
       "      <td id=\"T_6809b_row1_col5\" class=\"data row1 col5\" >-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row2\" class=\"row_heading level0 row2\" >PST</th>\n",
       "      <td id=\"T_6809b_row2_col0\" class=\"data row2 col0\" >0.803791</td>\n",
       "      <td id=\"T_6809b_row2_col1\" class=\"data row2 col1\" >24.000000</td>\n",
       "      <td id=\"T_6809b_row2_col2\" class=\"data row2 col2\" >0.636871</td>\n",
       "      <td id=\"T_6809b_row2_col3\" class=\"data row2 col3\" >43.000000</td>\n",
       "      <td id=\"T_6809b_row2_col4\" class=\"data row2 col4\" >+19</td>\n",
       "      <td id=\"T_6809b_row2_col5\" class=\"data row2 col5\" >-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row3\" class=\"row_heading level0 row3\" >EncDec-AD</th>\n",
       "      <td id=\"T_6809b_row3_col0\" class=\"data row3 col0\" >0.877664</td>\n",
       "      <td id=\"T_6809b_row3_col1\" class=\"data row3 col1\" >10.000000</td>\n",
       "      <td id=\"T_6809b_row3_col2\" class=\"data row3 col2\" >0.716447</td>\n",
       "      <td id=\"T_6809b_row3_col3\" class=\"data row3 col3\" >23.000000</td>\n",
       "      <td id=\"T_6809b_row3_col4\" class=\"data row3 col4\" >+13</td>\n",
       "      <td id=\"T_6809b_row3_col5\" class=\"data row3 col5\" >-0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row4\" class=\"row_heading level0 row4\" >k-Means</th>\n",
       "      <td id=\"T_6809b_row4_col0\" class=\"data row4 col0\" >0.872913</td>\n",
       "      <td id=\"T_6809b_row4_col1\" class=\"data row4 col1\" >13.000000</td>\n",
       "      <td id=\"T_6809b_row4_col2\" class=\"data row4 col2\" >0.727221</td>\n",
       "      <td id=\"T_6809b_row4_col3\" class=\"data row4 col3\" >21.000000</td>\n",
       "      <td id=\"T_6809b_row4_col4\" class=\"data row4 col4\" >+08</td>\n",
       "      <td id=\"T_6809b_row4_col5\" class=\"data row4 col5\" >-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row5\" class=\"row_heading level0 row5\" >STAMP</th>\n",
       "      <td id=\"T_6809b_row5_col0\" class=\"data row5 col0\" >0.874142</td>\n",
       "      <td id=\"T_6809b_row5_col1\" class=\"data row5 col1\" >12.000000</td>\n",
       "      <td id=\"T_6809b_row5_col2\" class=\"data row5 col2\" >0.730938</td>\n",
       "      <td id=\"T_6809b_row5_col3\" class=\"data row5 col3\" >19.000000</td>\n",
       "      <td id=\"T_6809b_row5_col4\" class=\"data row5 col4\" >+07</td>\n",
       "      <td id=\"T_6809b_row5_col5\" class=\"data row5 col5\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row6\" class=\"row_heading level0 row6\" >STOMP</th>\n",
       "      <td id=\"T_6809b_row6_col0\" class=\"data row6 col0\" >0.874267</td>\n",
       "      <td id=\"T_6809b_row6_col1\" class=\"data row6 col1\" >11.000000</td>\n",
       "      <td id=\"T_6809b_row6_col2\" class=\"data row6 col2\" >0.735858</td>\n",
       "      <td id=\"T_6809b_row6_col3\" class=\"data row6 col3\" >18.000000</td>\n",
       "      <td id=\"T_6809b_row6_col4\" class=\"data row6 col4\" >+07</td>\n",
       "      <td id=\"T_6809b_row6_col5\" class=\"data row6 col5\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row7\" class=\"row_heading level0 row7\" >HOT SAX</th>\n",
       "      <td id=\"T_6809b_row7_col0\" class=\"data row7 col0\" >0.731207</td>\n",
       "      <td id=\"T_6809b_row7_col1\" class=\"data row7 col1\" >29.000000</td>\n",
       "      <td id=\"T_6809b_row7_col2\" class=\"data row7 col2\" >0.595371</td>\n",
       "      <td id=\"T_6809b_row7_col3\" class=\"data row7 col3\" >47.000000</td>\n",
       "      <td id=\"T_6809b_row7_col4\" class=\"data row7 col4\" >+18</td>\n",
       "      <td id=\"T_6809b_row7_col5\" class=\"data row7 col5\" >-0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row8\" class=\"row_heading level0 row8\" >SAND</th>\n",
       "      <td id=\"T_6809b_row8_col0\" class=\"data row8 col0\" >0.898257</td>\n",
       "      <td id=\"T_6809b_row8_col1\" class=\"data row8 col1\" >5.000000</td>\n",
       "      <td id=\"T_6809b_row8_col2\" class=\"data row8 col2\" >0.769493</td>\n",
       "      <td id=\"T_6809b_row8_col3\" class=\"data row8 col3\" >11.000000</td>\n",
       "      <td id=\"T_6809b_row8_col4\" class=\"data row8 col4\" >+06</td>\n",
       "      <td id=\"T_6809b_row8_col5\" class=\"data row8 col5\" >-0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row9\" class=\"row_heading level0 row9\" >Left STAMPi</th>\n",
       "      <td id=\"T_6809b_row9_col0\" class=\"data row9 col0\" >0.880459</td>\n",
       "      <td id=\"T_6809b_row9_col1\" class=\"data row9 col1\" >9.000000</td>\n",
       "      <td id=\"T_6809b_row9_col2\" class=\"data row9 col2\" >0.767743</td>\n",
       "      <td id=\"T_6809b_row9_col3\" class=\"data row9 col3\" >12.000000</td>\n",
       "      <td id=\"T_6809b_row9_col4\" class=\"data row9 col4\" >+03</td>\n",
       "      <td id=\"T_6809b_row9_col5\" class=\"data row9 col5\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row10\" class=\"row_heading level0 row10\" >Series2Graph</th>\n",
       "      <td id=\"T_6809b_row10_col0\" class=\"data row10 col0\" >0.861379</td>\n",
       "      <td id=\"T_6809b_row10_col1\" class=\"data row10 col1\" >16.000000</td>\n",
       "      <td id=\"T_6809b_row10_col2\" class=\"data row10 col2\" >0.749638</td>\n",
       "      <td id=\"T_6809b_row10_col3\" class=\"data row10 col3\" >13.000000</td>\n",
       "      <td id=\"T_6809b_row10_col4\" class=\"data row10 col4\" >-03</td>\n",
       "      <td id=\"T_6809b_row10_col5\" class=\"data row10 col5\" >-0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row11\" class=\"row_heading level0 row11\" >FFT</th>\n",
       "      <td id=\"T_6809b_row11_col0\" class=\"data row11 col0\" >0.644080</td>\n",
       "      <td id=\"T_6809b_row11_col1\" class=\"data row11 col1\" >37.000000</td>\n",
       "      <td id=\"T_6809b_row11_col2\" class=\"data row11 col2\" >0.545552</td>\n",
       "      <td id=\"T_6809b_row11_col3\" class=\"data row11 col3\" >52.000000</td>\n",
       "      <td id=\"T_6809b_row11_col4\" class=\"data row11 col4\" >+15</td>\n",
       "      <td id=\"T_6809b_row11_col5\" class=\"data row11 col5\" >-0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row12\" class=\"row_heading level0 row12\" >LSTM-AD</th>\n",
       "      <td id=\"T_6809b_row12_col0\" class=\"data row12 col0\" >0.965738</td>\n",
       "      <td id=\"T_6809b_row12_col1\" class=\"data row12 col1\" >1.000000</td>\n",
       "      <td id=\"T_6809b_row12_col2\" class=\"data row12 col2\" >0.874218</td>\n",
       "      <td id=\"T_6809b_row12_col3\" class=\"data row12 col3\" >2.000000</td>\n",
       "      <td id=\"T_6809b_row12_col4\" class=\"data row12 col4\" >+01</td>\n",
       "      <td id=\"T_6809b_row12_col5\" class=\"data row12 col5\" >-0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row13\" class=\"row_heading level0 row13\" >DWT-MLEAD</th>\n",
       "      <td id=\"T_6809b_row13_col0\" class=\"data row13 col0\" >0.907602</td>\n",
       "      <td id=\"T_6809b_row13_col1\" class=\"data row13 col1\" >4.000000</td>\n",
       "      <td id=\"T_6809b_row13_col2\" class=\"data row13 col2\" >0.829061</td>\n",
       "      <td id=\"T_6809b_row13_col3\" class=\"data row13 col3\" >7.000000</td>\n",
       "      <td id=\"T_6809b_row13_col4\" class=\"data row13 col4\" >+03</td>\n",
       "      <td id=\"T_6809b_row13_col5\" class=\"data row13 col5\" >-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row14\" class=\"row_heading level0 row14\" >Telemanom</th>\n",
       "      <td id=\"T_6809b_row14_col0\" class=\"data row14 col0\" >0.863892</td>\n",
       "      <td id=\"T_6809b_row14_col1\" class=\"data row14 col1\" >15.000000</td>\n",
       "      <td id=\"T_6809b_row14_col2\" class=\"data row14 col2\" >0.786597</td>\n",
       "      <td id=\"T_6809b_row14_col3\" class=\"data row14 col3\" >9.000000</td>\n",
       "      <td id=\"T_6809b_row14_col4\" class=\"data row14 col4\" >-06</td>\n",
       "      <td id=\"T_6809b_row14_col5\" class=\"data row14 col5\" >-0.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row15\" class=\"row_heading level0 row15\" >ARIMA</th>\n",
       "      <td id=\"T_6809b_row15_col0\" class=\"data row15 col0\" >0.816814</td>\n",
       "      <td id=\"T_6809b_row15_col1\" class=\"data row15 col1\" >23.000000</td>\n",
       "      <td id=\"T_6809b_row15_col2\" class=\"data row15 col2\" >0.748304</td>\n",
       "      <td id=\"T_6809b_row15_col3\" class=\"data row15 col3\" >14.000000</td>\n",
       "      <td id=\"T_6809b_row15_col4\" class=\"data row15 col4\" >-09</td>\n",
       "      <td id=\"T_6809b_row15_col5\" class=\"data row15 col5\" >-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row16\" class=\"row_heading level0 row16\" >GrammarViz</th>\n",
       "      <td id=\"T_6809b_row16_col0\" class=\"data row16 col0\" >0.894852</td>\n",
       "      <td id=\"T_6809b_row16_col1\" class=\"data row16 col1\" >7.000000</td>\n",
       "      <td id=\"T_6809b_row16_col2\" class=\"data row16 col2\" >0.829320</td>\n",
       "      <td id=\"T_6809b_row16_col3\" class=\"data row16 col3\" >6.000000</td>\n",
       "      <td id=\"T_6809b_row16_col4\" class=\"data row16 col4\" >-01</td>\n",
       "      <td id=\"T_6809b_row16_col5\" class=\"data row16 col5\" >-0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row17\" class=\"row_heading level0 row17\" >LaserDBN</th>\n",
       "      <td id=\"T_6809b_row17_col0\" class=\"data row17 col0\" >0.655523</td>\n",
       "      <td id=\"T_6809b_row17_col1\" class=\"data row17 col1\" >35.000000</td>\n",
       "      <td id=\"T_6809b_row17_col2\" class=\"data row17 col2\" >0.595319</td>\n",
       "      <td id=\"T_6809b_row17_col3\" class=\"data row17 col3\" >48.000000</td>\n",
       "      <td id=\"T_6809b_row17_col4\" class=\"data row17 col4\" >+13</td>\n",
       "      <td id=\"T_6809b_row17_col5\" class=\"data row17 col5\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row18\" class=\"row_heading level0 row18\" >OceanWNN</th>\n",
       "      <td id=\"T_6809b_row18_col0\" class=\"data row18 col0\" >0.734238</td>\n",
       "      <td id=\"T_6809b_row18_col1\" class=\"data row18 col1\" >28.000000</td>\n",
       "      <td id=\"T_6809b_row18_col2\" class=\"data row18 col2\" >0.677339</td>\n",
       "      <td id=\"T_6809b_row18_col3\" class=\"data row18 col3\" >30.000000</td>\n",
       "      <td id=\"T_6809b_row18_col4\" class=\"data row18 col4\" >+02</td>\n",
       "      <td id=\"T_6809b_row18_col5\" class=\"data row18 col5\" >-0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row19\" class=\"row_heading level0 row19\" >DBStream</th>\n",
       "      <td id=\"T_6809b_row19_col0\" class=\"data row19 col0\" >0.719925</td>\n",
       "      <td id=\"T_6809b_row19_col1\" class=\"data row19 col1\" >31.000000</td>\n",
       "      <td id=\"T_6809b_row19_col2\" class=\"data row19 col2\" >0.665081</td>\n",
       "      <td id=\"T_6809b_row19_col3\" class=\"data row19 col3\" >36.000000</td>\n",
       "      <td id=\"T_6809b_row19_col4\" class=\"data row19 col4\" >+05</td>\n",
       "      <td id=\"T_6809b_row19_col5\" class=\"data row19 col5\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row20\" class=\"row_heading level0 row20\" >Donut</th>\n",
       "      <td id=\"T_6809b_row20_col0\" class=\"data row20 col0\" >0.894965</td>\n",
       "      <td id=\"T_6809b_row20_col1\" class=\"data row20 col1\" >6.000000</td>\n",
       "      <td id=\"T_6809b_row20_col2\" class=\"data row20 col2\" >0.843879</td>\n",
       "      <td id=\"T_6809b_row20_col3\" class=\"data row20 col3\" >4.000000</td>\n",
       "      <td id=\"T_6809b_row20_col4\" class=\"data row20 col4\" >-02</td>\n",
       "      <td id=\"T_6809b_row20_col5\" class=\"data row20 col5\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row21\" class=\"row_heading level0 row21\" >DeepAnT</th>\n",
       "      <td id=\"T_6809b_row21_col0\" class=\"data row21 col0\" >0.726896</td>\n",
       "      <td id=\"T_6809b_row21_col1\" class=\"data row21 col1\" >30.000000</td>\n",
       "      <td id=\"T_6809b_row21_col2\" class=\"data row21 col2\" >0.676560</td>\n",
       "      <td id=\"T_6809b_row21_col3\" class=\"data row21 col3\" >31.000000</td>\n",
       "      <td id=\"T_6809b_row21_col4\" class=\"data row21 col4\" >+01</td>\n",
       "      <td id=\"T_6809b_row21_col5\" class=\"data row21 col5\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row22\" class=\"row_heading level0 row22\" >TSBitmap</th>\n",
       "      <td id=\"T_6809b_row22_col0\" class=\"data row22 col0\" >0.637278</td>\n",
       "      <td id=\"T_6809b_row22_col1\" class=\"data row22 col1\" >39.000000</td>\n",
       "      <td id=\"T_6809b_row22_col2\" class=\"data row22 col2\" >0.588103</td>\n",
       "      <td id=\"T_6809b_row22_col3\" class=\"data row22 col3\" >49.000000</td>\n",
       "      <td id=\"T_6809b_row22_col4\" class=\"data row22 col4\" >+10</td>\n",
       "      <td id=\"T_6809b_row22_col5\" class=\"data row22 col5\" >-0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row23\" class=\"row_heading level0 row23\" >VALMOD</th>\n",
       "      <td id=\"T_6809b_row23_col0\" class=\"data row23 col0\" >0.858050</td>\n",
       "      <td id=\"T_6809b_row23_col1\" class=\"data row23 col1\" >18.000000</td>\n",
       "      <td id=\"T_6809b_row23_col2\" class=\"data row23 col2\" >0.813582</td>\n",
       "      <td id=\"T_6809b_row23_col3\" class=\"data row23 col3\" >8.000000</td>\n",
       "      <td id=\"T_6809b_row23_col4\" class=\"data row23 col4\" >-10</td>\n",
       "      <td id=\"T_6809b_row23_col5\" class=\"data row23 col5\" >-0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row24\" class=\"row_heading level0 row24\" >HealthESN</th>\n",
       "      <td id=\"T_6809b_row24_col0\" class=\"data row24 col0\" >0.853132</td>\n",
       "      <td id=\"T_6809b_row24_col1\" class=\"data row24 col1\" >20.000000</td>\n",
       "      <td id=\"T_6809b_row24_col2\" class=\"data row24 col2\" >0.832034</td>\n",
       "      <td id=\"T_6809b_row24_col3\" class=\"data row24 col3\" >5.000000</td>\n",
       "      <td id=\"T_6809b_row24_col4\" class=\"data row24 col4\" >-15</td>\n",
       "      <td id=\"T_6809b_row24_col5\" class=\"data row24 col5\" >-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row25\" class=\"row_heading level0 row25\" >OmniAnomaly</th>\n",
       "      <td id=\"T_6809b_row25_col0\" class=\"data row25 col0\" >0.644023</td>\n",
       "      <td id=\"T_6809b_row25_col1\" class=\"data row25 col1\" >38.000000</td>\n",
       "      <td id=\"T_6809b_row25_col2\" class=\"data row25 col2\" >0.629753</td>\n",
       "      <td id=\"T_6809b_row25_col3\" class=\"data row25 col3\" >44.000000</td>\n",
       "      <td id=\"T_6809b_row25_col4\" class=\"data row25 col4\" >+06</td>\n",
       "      <td id=\"T_6809b_row25_col5\" class=\"data row25 col5\" >-0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row26\" class=\"row_heading level0 row26\" >DSPOT</th>\n",
       "      <td id=\"T_6809b_row26_col0\" class=\"data row26 col0\" >0.554605</td>\n",
       "      <td id=\"T_6809b_row26_col1\" class=\"data row26 col1\" >51.000000</td>\n",
       "      <td id=\"T_6809b_row26_col2\" class=\"data row26 col2\" >0.550928</td>\n",
       "      <td id=\"T_6809b_row26_col3\" class=\"data row26 col3\" >51.000000</td>\n",
       "      <td id=\"T_6809b_row26_col4\" class=\"data row26 col4\" >+00</td>\n",
       "      <td id=\"T_6809b_row26_col5\" class=\"data row26 col5\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row27\" class=\"row_heading level0 row27\" >MultiHMM</th>\n",
       "      <td id=\"T_6809b_row27_col0\" class=\"data row27 col0\" >0.478073</td>\n",
       "      <td id=\"T_6809b_row27_col1\" class=\"data row27 col1\" >58.000000</td>\n",
       "      <td id=\"T_6809b_row27_col2\" class=\"data row27 col2\" >0.476958</td>\n",
       "      <td id=\"T_6809b_row27_col3\" class=\"data row27 col3\" >60.000000</td>\n",
       "      <td id=\"T_6809b_row27_col4\" class=\"data row27 col4\" >+02</td>\n",
       "      <td id=\"T_6809b_row27_col5\" class=\"data row27 col5\" >-0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row28\" class=\"row_heading level0 row28\" >Normalizing Flows</th>\n",
       "      <td id=\"T_6809b_row28_col0\" class=\"data row28 col0\" >0.869716</td>\n",
       "      <td id=\"T_6809b_row28_col1\" class=\"data row28 col1\" >14.000000</td>\n",
       "      <td id=\"T_6809b_row28_col2\" class=\"data row28 col2\" >0.871548</td>\n",
       "      <td id=\"T_6809b_row28_col3\" class=\"data row28 col3\" >3.000000</td>\n",
       "      <td id=\"T_6809b_row28_col4\" class=\"data row28 col4\" >-11</td>\n",
       "      <td id=\"T_6809b_row28_col5\" class=\"data row28 col5\" >+0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row29\" class=\"row_heading level0 row29\" >SR-CNN</th>\n",
       "      <td id=\"T_6809b_row29_col0\" class=\"data row29 col0\" >0.502331</td>\n",
       "      <td id=\"T_6809b_row29_col1\" class=\"data row29 col1\" >56.000000</td>\n",
       "      <td id=\"T_6809b_row29_col2\" class=\"data row29 col2\" >0.509170</td>\n",
       "      <td id=\"T_6809b_row29_col3\" class=\"data row29 col3\" >57.000000</td>\n",
       "      <td id=\"T_6809b_row29_col4\" class=\"data row29 col4\" >+01</td>\n",
       "      <td id=\"T_6809b_row29_col5\" class=\"data row29 col5\" >+0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row30\" class=\"row_heading level0 row30\" >Bagel</th>\n",
       "      <td id=\"T_6809b_row30_col0\" class=\"data row30 col0\" >0.525684</td>\n",
       "      <td id=\"T_6809b_row30_col1\" class=\"data row30 col1\" >54.000000</td>\n",
       "      <td id=\"T_6809b_row30_col2\" class=\"data row30 col2\" >0.540086</td>\n",
       "      <td id=\"T_6809b_row30_col3\" class=\"data row30 col3\" >54.000000</td>\n",
       "      <td id=\"T_6809b_row30_col4\" class=\"data row30 col4\" >+00</td>\n",
       "      <td id=\"T_6809b_row30_col5\" class=\"data row30 col5\" >+0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row31\" class=\"row_heading level0 row31\" >TARZAN</th>\n",
       "      <td id=\"T_6809b_row31_col0\" class=\"data row31 col0\" >0.474698</td>\n",
       "      <td id=\"T_6809b_row31_col1\" class=\"data row31 col1\" >59.000000</td>\n",
       "      <td id=\"T_6809b_row31_col2\" class=\"data row31 col2\" >0.502058</td>\n",
       "      <td id=\"T_6809b_row31_col3\" class=\"data row31 col3\" >58.000000</td>\n",
       "      <td id=\"T_6809b_row31_col4\" class=\"data row31 col4\" >-01</td>\n",
       "      <td id=\"T_6809b_row31_col5\" class=\"data row31 col5\" >+0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row32\" class=\"row_heading level0 row32\" >MedianMethod</th>\n",
       "      <td id=\"T_6809b_row32_col0\" class=\"data row32 col0\" >0.648901</td>\n",
       "      <td id=\"T_6809b_row32_col1\" class=\"data row32 col1\" >36.000000</td>\n",
       "      <td id=\"T_6809b_row32_col2\" class=\"data row32 col2\" >0.676325</td>\n",
       "      <td id=\"T_6809b_row32_col3\" class=\"data row32 col3\" >33.000000</td>\n",
       "      <td id=\"T_6809b_row32_col4\" class=\"data row32 col4\" >-03</td>\n",
       "      <td id=\"T_6809b_row32_col5\" class=\"data row32 col5\" >+0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row33\" class=\"row_heading level0 row33\" >RobustPCA</th>\n",
       "      <td id=\"T_6809b_row33_col0\" class=\"data row33 col0\" >0.514437</td>\n",
       "      <td id=\"T_6809b_row33_col1\" class=\"data row33 col1\" >55.000000</td>\n",
       "      <td id=\"T_6809b_row33_col2\" class=\"data row33 col2\" >0.542928</td>\n",
       "      <td id=\"T_6809b_row33_col3\" class=\"data row33 col3\" >53.000000</td>\n",
       "      <td id=\"T_6809b_row33_col4\" class=\"data row33 col4\" >-02</td>\n",
       "      <td id=\"T_6809b_row33_col5\" class=\"data row33 col5\" >+0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row34\" class=\"row_heading level0 row34\" >PCI</th>\n",
       "      <td id=\"T_6809b_row34_col0\" class=\"data row34 col0\" >0.696556</td>\n",
       "      <td id=\"T_6809b_row34_col1\" class=\"data row34 col1\" >32.000000</td>\n",
       "      <td id=\"T_6809b_row34_col2\" class=\"data row34 col2\" >0.738317</td>\n",
       "      <td id=\"T_6809b_row34_col3\" class=\"data row34 col3\" >17.000000</td>\n",
       "      <td id=\"T_6809b_row34_col4\" class=\"data row34 col4\" >-15</td>\n",
       "      <td id=\"T_6809b_row34_col5\" class=\"data row34 col5\" >+0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row35\" class=\"row_heading level0 row35\" >NumentaHTM</th>\n",
       "      <td id=\"T_6809b_row35_col0\" class=\"data row35 col0\" >0.670671</td>\n",
       "      <td id=\"T_6809b_row35_col1\" class=\"data row35 col1\" >34.000000</td>\n",
       "      <td id=\"T_6809b_row35_col2\" class=\"data row35 col2\" >0.714026</td>\n",
       "      <td id=\"T_6809b_row35_col3\" class=\"data row35 col3\" >24.000000</td>\n",
       "      <td id=\"T_6809b_row35_col4\" class=\"data row35 col4\" >-10</td>\n",
       "      <td id=\"T_6809b_row35_col5\" class=\"data row35 col5\" >+0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row36\" class=\"row_heading level0 row36\" >Hybrid KNN</th>\n",
       "      <td id=\"T_6809b_row36_col0\" class=\"data row36 col0\" >0.449687</td>\n",
       "      <td id=\"T_6809b_row36_col1\" class=\"data row36 col1\" >60.000000</td>\n",
       "      <td id=\"T_6809b_row36_col2\" class=\"data row36 col2\" >0.496522</td>\n",
       "      <td id=\"T_6809b_row36_col3\" class=\"data row36 col3\" >59.000000</td>\n",
       "      <td id=\"T_6809b_row36_col4\" class=\"data row36 col4\" >-01</td>\n",
       "      <td id=\"T_6809b_row36_col5\" class=\"data row36 col5\" >+0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row37\" class=\"row_heading level0 row37\" >CBLOF</th>\n",
       "      <td id=\"T_6809b_row37_col0\" class=\"data row37 col0\" >0.606390</td>\n",
       "      <td id=\"T_6809b_row37_col1\" class=\"data row37 col1\" >42.000000</td>\n",
       "      <td id=\"T_6809b_row37_col2\" class=\"data row37 col2\" >0.656691</td>\n",
       "      <td id=\"T_6809b_row37_col3\" class=\"data row37 col3\" >37.000000</td>\n",
       "      <td id=\"T_6809b_row37_col4\" class=\"data row37 col4\" >-05</td>\n",
       "      <td id=\"T_6809b_row37_col5\" class=\"data row37 col5\" >+0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row38\" class=\"row_heading level0 row38\" >PCC</th>\n",
       "      <td id=\"T_6809b_row38_col0\" class=\"data row38 col0\" >0.532033</td>\n",
       "      <td id=\"T_6809b_row38_col1\" class=\"data row38 col1\" >53.000000</td>\n",
       "      <td id=\"T_6809b_row38_col2\" class=\"data row38 col2\" >0.586911</td>\n",
       "      <td id=\"T_6809b_row38_col3\" class=\"data row38 col3\" >50.000000</td>\n",
       "      <td id=\"T_6809b_row38_col4\" class=\"data row38 col4\" >-03</td>\n",
       "      <td id=\"T_6809b_row38_col5\" class=\"data row38 col5\" >+0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row39\" class=\"row_heading level0 row39\" >IF-LOF</th>\n",
       "      <td id=\"T_6809b_row39_col0\" class=\"data row39 col0\" >0.587309</td>\n",
       "      <td id=\"T_6809b_row39_col1\" class=\"data row39 col1\" >46.000000</td>\n",
       "      <td id=\"T_6809b_row39_col2\" class=\"data row39 col2\" >0.650615</td>\n",
       "      <td id=\"T_6809b_row39_col3\" class=\"data row39 col3\" >40.000000</td>\n",
       "      <td id=\"T_6809b_row39_col4\" class=\"data row39 col4\" >-06</td>\n",
       "      <td id=\"T_6809b_row39_col5\" class=\"data row39 col5\" >+0.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row40\" class=\"row_heading level0 row40\" >KNN</th>\n",
       "      <td id=\"T_6809b_row40_col0\" class=\"data row40 col0\" >0.614195</td>\n",
       "      <td id=\"T_6809b_row40_col1\" class=\"data row40 col1\" >40.000000</td>\n",
       "      <td id=\"T_6809b_row40_col2\" class=\"data row40 col2\" >0.682972</td>\n",
       "      <td id=\"T_6809b_row40_col3\" class=\"data row40 col3\" >27.000000</td>\n",
       "      <td id=\"T_6809b_row40_col4\" class=\"data row40 col4\" >-13</td>\n",
       "      <td id=\"T_6809b_row40_col5\" class=\"data row40 col5\" >+0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row41\" class=\"row_heading level0 row41\" >LOF</th>\n",
       "      <td id=\"T_6809b_row41_col0\" class=\"data row41 col0\" >0.577457</td>\n",
       "      <td id=\"T_6809b_row41_col1\" class=\"data row41 col1\" >47.000000</td>\n",
       "      <td id=\"T_6809b_row41_col2\" class=\"data row41 col2\" >0.646805</td>\n",
       "      <td id=\"T_6809b_row41_col3\" class=\"data row41 col3\" >41.000000</td>\n",
       "      <td id=\"T_6809b_row41_col4\" class=\"data row41 col4\" >-06</td>\n",
       "      <td id=\"T_6809b_row41_col5\" class=\"data row41 col5\" >+0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row42\" class=\"row_heading level0 row42\" >HBOS</th>\n",
       "      <td id=\"T_6809b_row42_col0\" class=\"data row42 col0\" >0.599450</td>\n",
       "      <td id=\"T_6809b_row42_col1\" class=\"data row42 col1\" >44.000000</td>\n",
       "      <td id=\"T_6809b_row42_col2\" class=\"data row42 col2\" >0.672752</td>\n",
       "      <td id=\"T_6809b_row42_col3\" class=\"data row42 col3\" >34.000000</td>\n",
       "      <td id=\"T_6809b_row42_col4\" class=\"data row42 col4\" >-10</td>\n",
       "      <td id=\"T_6809b_row42_col5\" class=\"data row42 col5\" >+0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row43\" class=\"row_heading level0 row43\" >COF</th>\n",
       "      <td id=\"T_6809b_row43_col0\" class=\"data row43 col0\" >0.555700</td>\n",
       "      <td id=\"T_6809b_row43_col1\" class=\"data row43 col1\" >50.000000</td>\n",
       "      <td id=\"T_6809b_row43_col2\" class=\"data row43 col2\" >0.643751</td>\n",
       "      <td id=\"T_6809b_row43_col3\" class=\"data row43 col3\" >42.000000</td>\n",
       "      <td id=\"T_6809b_row43_col4\" class=\"data row43 col4\" >-08</td>\n",
       "      <td id=\"T_6809b_row43_col5\" class=\"data row43 col5\" >+0.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6809b_level0_row44\" class=\"row_heading level0 row44\" >COPOD</th>\n",
       "      <td id=\"T_6809b_row44_col0\" class=\"data row44 col0\" >0.543097</td>\n",
       "      <td id=\"T_6809b_row44_col1\" class=\"data row44 col1\" >52.000000</td>\n",
       "      <td id=\"T_6809b_row44_col2\" class=\"data row44 col2\" >0.653691</td>\n",
       "      <td id=\"T_6809b_row44_col3\" class=\"data row44 col3\" >39.000000</td>\n",
       "      <td id=\"T_6809b_row44_col4\" class=\"data row44 col4\" >-13</td>\n",
       "      <td id=\"T_6809b_row44_col5\" class=\"data row44 col5\" >+0.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f514bfa0dc0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp = df.groupby(by=\"algorithm\")[[\"ROC_AUC\"]].mean()\n",
    "df_tmp = df_tmp.sort_values(by=\"ROC_AUC\", ascending=False)\n",
    "df_tmp[\"Rank\"] = df_tmp.rank(ascending=False)\n",
    "\n",
    "df_tmp_gt = df[df[\"collection\"] == \"GutenTAG\"].groupby(by=\"algo_display_name\")[[\"ROC_AUC\"]].mean()\n",
    "df_tmp_gt = df_tmp_gt.sort_values(by=\"ROC_AUC\", ascending=False)\n",
    "df_tmp_gt[\"Rank\"] = df_tmp_gt.rank(ascending=False)\n",
    "df_tmp = pd.merge(df_tmp_gt, df_tmp, left_index=True, right_index=True, how=\"inner\", suffixes=(\"_gt\", \"_all\"))\n",
    "df_tmp[\"Diff_rank\"] = df_tmp[\"Rank_all\"] - df_tmp[\"Rank_gt\"]\n",
    "df_tmp[\"Diff ROC_AUC\"] = df_tmp[\"ROC_AUC_all\"] - df_tmp[\"ROC_AUC_gt\"]\n",
    "df_tmp.sort_values(\"Diff ROC_AUC\").style.format({\"Diff_rank\": \"{:+03.0f}\".format, \"Diff ROC_AUC\": \"{:+0.2f}\".format})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reliability of our metric scores and ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def method_family_marker_map(family):\n",
    "    mapping = {\n",
    "        \"encoding\": \"v\",\n",
    "        \"distance\": \"o\",\n",
    "        \"distribution\": \"^\",\n",
    "        \"forecasting\": \"*\",\n",
    "        \"reconstruction\": \"s\",\n",
    "        \"trees\": \"P\"\n",
    "    }\n",
    "    return mapping[family]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormap = method_family_colormap\n",
    "method_families = sorted(set([algo_meta[algo][\"method_family\"] for algo in algo_meta]) - {\"baseline\"})\n",
    "annotated_algorithms = [\n",
    "    \"TAnoGAN\",\n",
    "    \"EncDec-AD\",\n",
    "    \"LSTM-AD\",\n",
    "    \"HealthESN\",\n",
    "    \"MultiHMM\",\n",
    "    \"Normalizing Flows\",\n",
    "    \"DBStream\",\n",
    "    \"S-H-ESD\",\n",
    "    \"DWT-MLEAD\",\n",
    "    \"RobustPCA\"\n",
    "]\n",
    "\n",
    "algo_auroc = df_asl.T.mean()\n",
    "dataset_count_lut = df_algorithm_error_counts[\"OK\"] / df_algorithm_error_counts[\"ALL\"]\n",
    "cycler = plt.cycler(marker=[\"o\", \"+\", \"*\", \"x\", \".\", \"X\"]) * plt.cycler(color=plt.get_cmap(\"tab20\").colors)\n",
    "\n",
    "df_tmp = algo_auroc.copy().to_frame(\"auroc\")\n",
    "df_tmp.loc[:, \"reliability\"] = df_tmp.index.map(lambda x: dataset_count_lut[x[2]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87% of all algorithms reached a reliability of above 0.7.\n",
      "35% of all algorithms reached a reliability of above 0.99.\n",
      "DWT-MLEAD has a ROC_AUC of 0.83 with a reliability of 100%.\n",
      "RobustPCA has a ROC_AUC of 0.54 with a reliability of 100%.\n",
      "S-H-ESD (Twitter) has a ROC_AUC of 0.53 with a reliability of 51%.\n",
      "HealthESN has a ROC_AUC of 0.83 with a reliability of 34%.\n",
      "13% (8) of all algorithms reached a reliability of below 0.52.\n"
     ]
    }
   ],
   "source": [
    "def percentage_above(pct):\n",
    "    print(f\"{len(df_tmp[df_tmp.reliability > pct]) / len(df_tmp):.0%} of all algorithms reached a reliability of above {pct}.\")\n",
    "def percentage_below(pct):\n",
    "    print(f\"{len(df_tmp[df_tmp.reliability < pct]) / len(df_tmp):.0%} ({len(df_tmp[df_tmp.reliability < pct])}) of all algorithms reached a reliability of below {pct}.\")\n",
    "\n",
    "percentage_above(0.7)\n",
    "percentage_above(0.99)\n",
    "\n",
    "def algorithm_auroc_reliability(alg):\n",
    "    s = df_tmp.xs(alg, level=\"algorithm\")\n",
    "    print(f\"{alg} has a ROC_AUC of {s.auroc.iloc[0]:.2f} with a reliability of {s.reliability.iloc[0]:.0%}.\")\n",
    "\n",
    "algorithm_auroc_reliability(\"DWT-MLEAD\")\n",
    "algorithm_auroc_reliability(\"RobustPCA\")\n",
    "algorithm_auroc_reliability(\"S-H-ESD (Twitter)\")\n",
    "algorithm_auroc_reliability(\"HealthESN\")\n",
    "\n",
    "percentage_below(0.52)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(single_column_figwidth, 5))\n",
    "ax = fig.gca()\n",
    "ax.set_prop_cycle(cycler)\n",
    "\n",
    "scatter_plots = defaultdict(lambda: defaultdict(list))\n",
    "algo_positions = {}\n",
    "\n",
    "for (_, _, algo), r in df_tmp.iterrows():\n",
    "    algo_metadata = algo_meta[algo]\n",
    "    name = algo_metadata[\"display_name\"]\n",
    "    method_family = algo_metadata[\"method_family\"]\n",
    "    x = r.auroc\n",
    "    y = r.reliability\n",
    "    scatter_plots[method_family][\"xs\"].append(x)\n",
    "    scatter_plots[method_family][\"ys\"].append(y)\n",
    "    scatter_plots[method_family][\"cs\"].append(colormap(method_families.index(method_family)))\n",
    "    if name in annotated_algorithms:\n",
    "        algo_positions[name] = (x, y)\n",
    "\n",
    "for family, scatter in scatter_plots.items():\n",
    "    xs = scatter[\"xs\"]\n",
    "    ys = scatter[\"ys\"]\n",
    "    cs = scatter[\"cs\"]\n",
    "    ax.scatter(xs, ys, color=cs, s=40, marker=method_family_marker_map(family))\n",
    "\n",
    "ax.legend(handles=[\n",
    "    Line2D([0], [0], color=\"w\", marker=method_family_marker_map(fam), markerfacecolor=colormap(i), markeredgecolor=colormap(i), label=fam, markersize=8)\n",
    "    for i, fam in enumerate(method_families)\n",
    "], loc=\"upper right\")\n",
    "\n",
    "for a in annotated_algorithms:\n",
    "    ha = \"left\"\n",
    "    text_position = (0, 5)\n",
    "    if a in [\"LSTM-AD\", \"MultiHMM\", \"EncDec-AD\", \"TAnoGAN\"]:\n",
    "        text_position = (0, -15)\n",
    "    if a in [\"HealthESN\"]:\n",
    "        ha = \"right\"\n",
    "    ax.annotate(a, algo_positions[a], textcoords=\"offset points\", xytext=text_position, ha=ha)\n",
    "\n",
    "# add vline to separate bad and good algos\n",
    "#ax.vlines([0.75], 0, 1, colors=\"grey\", linestyles=\"dashed\")\n",
    "ax.hlines([0.5], 0.45, 1, colors=\"grey\", linestyles=\"dashed\")\n",
    "ax.set_xlabel(\"AUC-ROC\")\n",
    "ax.set_ylabel(\"Successfully processed datasets (relative)\")\n",
    "#ax.set_title(\"Reliability of the ROC_AUC values\")\n",
    "ax.set_xlim(0.44, 1.065)\n",
    "ax.set_ylim(-0.05, 1.08)\n",
    "#ax.legend(ncol=2, loc=\"upper left\", bbox_to_anchor=(1, 1.01))\n",
    "fig.savefig(\"plots/reliability.pdf\", bbox_inches=\"tight\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Anomaly types and data characteristics\n",
    "\n",
    "- the heat-scatter plot ;)\n",
    "- what to annotate if multiple have 100% ROC_AUC? Take into account variance, runtime, errors, name multiple?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormap = method_family_colormap\n",
    "method_families = sorted(set([algo_meta[algo][\"method_family\"] for algo in algo_meta]) - {\"baseline\"})\n",
    "\n",
    "df_an = df_gt[df_gt.dataset.str.contains(\"-type-\")]\n",
    "anomaly_types = df_an.dataset.apply(lambda x: x.split(\"-type-\")[1]).unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_bo = df_gt.copy()\n",
    "df_bo[\"base_oscillation\"] = df_bo.dataset.apply(lambda x: x.split(\"-\")[0])\n",
    "base_oscillations = df_bo.base_oscillation.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tr = df_gt.copy()\n",
    "trend_data = df_gt[df_gt.dataset.str.contains(\"-trend-\")].dataset\n",
    "no_trend_data = ['sinus-noise-00%', 'sinus-noise-01%', 'sinus-noise-10%', 'sinus-noise-30%', 'sinus-noise-50%', 'sinus-same-count-1', 'sinus-same-count-2', 'sinus-same-count-5', 'sinus-same-count-10', 'sinus-diff-count-1', 'sinus-diff-count-2', 'sinus-diff-count-3', 'sinus-diff-count-4', 'sinus-diff-count-5', 'sinus-diff-count-6', 'sinus-diff-count-7', 'sinus-diff-count-8', 'sinus-diff-count-9', 'sinus-combined-diff-1', 'sinus-combined-diff-2', 'sinus-combined-diff-3', 'sinus-length-1', 'sinus-length-10', 'sinus-length-50', 'sinus-length-100', 'sinus-length-500', 'sinus-length-1000', 'sinus-type-amplitude', 'sinus-type-extremum', 'sinus-type-frequency', 'sinus-type-mean', 'sinus-type-pattern', 'sinus-type-pattern-shift', 'sinus-type-platform', 'sinus-type-trend', 'sinus-type-variance', 'sinus-channels-all-of-3', 'sinus-channels-single-of-2', 'sinus-channels-single-of-5', 'sinus-channels-single-of-10', 'sinus-channels-single-of-20', 'ecg-same-count-1', 'ecg-same-count-2', 'ecg-same-count-5', 'ecg-same-count-10', 'ecg-diff-count-1', 'ecg-diff-count-2', 'ecg-diff-count-3', 'ecg-diff-count-4', 'ecg-diff-count-5', 'ecg-diff-count-6', 'ecg-diff-count-7', 'ecg-diff-count-8', 'ecg-diff-count-9', 'ecg-combined-diff-2', 'ecg-combined-diff-3', 'ecg-length-1', 'ecg-length-10', 'ecg-length-50', 'ecg-length-100', 'ecg-length-500', 'ecg-length-1000', 'ecg-type-amplitude', 'ecg-type-extremum', 'ecg-type-frequency', 'ecg-type-mean', 'ecg-type-pattern', 'ecg-type-pattern-shift', 'ecg-type-platform', 'ecg-type-trend', 'ecg-type-variance', 'ecg-channels-all-of-3', 'ecg-channels-single-of-2', 'ecg-channels-single-of-5', 'ecg-channels-single-of-10', 'ecg-channels-single-of-20', 'cbf-same-count-1', 'cbf-same-count-2', 'cbf-same-count-5', 'cbf-same-count-10', 'cbf-diff-count-1', 'cbf-diff-count-2', 'cbf-diff-count-3', 'cbf-diff-count-4', 'cbf-diff-count-5', 'cbf-diff-count-6', 'cbf-diff-count-7', 'cbf-combined-diff-1', 'cbf-combined-diff-2', 'cbf-combined-diff-3', 'cbf-length-1', 'cbf-length-10', 'cbf-length-50', 'cbf-length-100', 'cbf-length-500', 'cbf-length-1000', 'cbf-type-amplitude', 'cbf-type-extremum', 'cbf-type-mean', 'cbf-type-pattern', 'cbf-type-platform', 'cbf-type-trend', 'cbf-type-variance', 'cbf-channels-all-of-3', 'cbf-channels-single-of-2', 'cbf-channels-single-of-5', 'cbf-channels-single-of-10', 'cbf-channels-single-of-20', 'rw-same-count-1', 'rw-same-count-2', 'rw-same-count-5', 'rw-same-count-10', 'rw-diff-count-1', 'rw-diff-count-2', 'rw-diff-count-3', 'rw-diff-count-4', 'rw-diff-count-5', 'rw-diff-count-6', 'rw-combined-diff-1', 'rw-combined-diff-2', 'rw-combined-diff-3', 'rw-length-1', 'rw-length-10', 'rw-length-50', 'rw-length-100', 'rw-length-500', 'rw-length-1000', 'rw-type-amplitude', 'rw-type-extremum', 'rw-type-mean', 'rw-type-platform', 'rw-type-trend', 'rw-type-variance', 'rw-channels-all-of-3', 'rw-channels-single-of-2', 'rw-channels-single-of-5', 'rw-channels-single-of-10', 'rw-channels-single-of-20', 'poly-same-count-1', 'poly-same-count-2', 'poly-same-count-5', 'poly-same-count-10', 'poly-diff-count-1', 'poly-diff-count-2', 'poly-diff-count-3', 'poly-diff-count-4', 'poly-diff-count-5', 'poly-combined-diff-1', 'poly-combined-diff-2', 'poly-combined-diff-3', 'poly-length-1', 'poly-length-10', 'poly-length-50', 'poly-length-100', 'poly-length-500', 'poly-length-1000', 'poly-type-extremum', 'poly-type-mean', 'poly-type-platform', 'poly-type-trend', 'poly-type-variance', 'poly-channels-all-of-3', 'poly-channels-single-of-2', 'poly-channels-single-of-5', 'poly-channels-single-of-10', 'poly-channels-single-of-20']\n",
    "df_tr.loc[df_tr.dataset.isin(trend_data), \"trend\"] = df_tr[df_tr.dataset.isin(trend_data)].dataset.apply(lambda x: x.split(\"-trend-\")[1])\n",
    "df_tr.loc[df_tr.dataset.isin(no_trend_data), \"trend\"] = \"no\"\n",
    "df_tr = df_tr[df_tr.trend.notna()]\n",
    "trends = df_tr.trend.unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['no', 'linear', 'quadratic', 'sinus']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "periodic_base_oscillations = [\"sinus\", \"ecg\"]\n",
    "nonperiodic_base_oscillations = [\"rw\", \"poly\", \"cbf\"]\n",
    "periodicity_lists = [periodic_base_oscillations, nonperiodic_base_oscillations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dimensions = df_gt.dataset_input_dimensionality.str.lower().unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "algorithm\n",
       "Extended Isolation Forest (EIF)    0.998380\n",
       "Hybrid Isolation Forest (HIF)      0.999330\n",
       "IF-LOF                             0.997760\n",
       "Isolation Forest (iForest)         0.998500\n",
       "PST                                0.805721\n",
       "Subsequence IF                     0.772657\n",
       "Name: ROC_AUC, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_an[df_an.dataset.str.contains(\"extremum\") & (df_an.algorithm.apply(lambda a: algo_meta[a][\"method_family\"] == \"trees\"))].groupby(\"algorithm\").mean()[\"ROC_AUC\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7516165504125756"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bo[df_bo.dataset.str.contains(\"rw-\")].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7057524073350329"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bo[df_bo.dataset.str.contains(\"ecg-\")].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7307143422346205"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bo[df_bo.dataset.apply(lambda x: x.split(\"-\")[0] in periodic_base_oscillations)].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6916415185380526"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bo[df_bo.dataset.apply(lambda x: x.split(\"-\")[0] in nonperiodic_base_oscillations)].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_bo[df_bo.dataset.apply(lambda x: x.split(\"-\")[0] in periodic_base_oscillations)].groupby(\"algorithm\").mean()[\"ROC_AUC\"] > 0.9).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_bo[df_bo.dataset.apply(lambda x: x.split(\"-\")[0] in nonperiodic_base_oscillations)].groupby(\"algorithm\").mean()[\"ROC_AUC\"] > 0.9).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7117028615498341"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gt[df_gt.dataset_input_dimensionality.str.lower() == \"univariate\"].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6495836318457076"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gt[df_gt.dataset_input_dimensionality.str.lower() == \"multivariate\"].groupby(\"algorithm\").mean()[\"ROC_AUC\"].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Per Algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def method_family_y(name=None, i=None):\n",
    "    CHARACTER_LINE_HEIGHT = 0.6\n",
    "    OFFSET = -1 + 0.3\n",
    "    if name is not None:\n",
    "        i = method_families.index(name)\n",
    "    \n",
    "    lineheight = (CHARACTER_LINE_HEIGHT / len(method_families))\n",
    "    return OFFSET - i * lineheight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_range = (0.4, 1.0)\n",
    "fontsize = plt.rcParams[\"font.size\"]\n",
    "xlim_ext_l = 0.05\n",
    "xlim_ext_r = 0.02\n",
    "\n",
    "scatter_plots = defaultdict(lambda: defaultdict(list))\n",
    "xs = []\n",
    "ys = []\n",
    "cs = []\n",
    "\n",
    "types = []\n",
    "best_per_type = []\n",
    "hlines = []\n",
    "means = []\n",
    "\n",
    "fig = plt.figure(figsize=(single_column_figwidth-2, 20))\n",
    "ax = fig.gca()\n",
    "\n",
    "def get_best_algorithm(grouped: pd.DataFrame) -> str:\n",
    "    auroc_mean = grouped[\"ROC_AUC\"].mean().reset_index()\n",
    "    max_auroc = auroc_mean.ROC_AUC.max()\n",
    "    \n",
    "    best_algorithms = auroc_mean[auroc_mean.ROC_AUC == max_auroc].algorithm.tolist()\n",
    "    \n",
    "    if len(best_algorithms) > 1:\n",
    "        # lowest std\n",
    "        auroc_std = grouped[\"ROC_AUC\"].std().reset_index()\n",
    "        auroc_std = auroc_std[(auroc_std.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = auroc_std[(auroc_std.ROC_AUC == auroc_std.ROC_AUC.min())].algorithm.tolist()\n",
    "        \n",
    "        # fewest errors\n",
    "        auroc_count = grouped[\"ROC_AUC\"].count().reset_index()\n",
    "        auroc_count = auroc_count[(auroc_count.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = auroc_count[(auroc_count.ROC_AUC == auroc_count.ROC_AUC.max())].algorithm.tolist()\n",
    "        \n",
    "        # fastest runtime\n",
    "        mean_time = grouped[\"overall_time\"].mean().reset_index()\n",
    "        mean_time = mean_time[(mean_time.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = mean_time[(mean_time.overall_time == mean_time.overall_time.min())].algorithm.tolist()\n",
    "    \n",
    "    best_algorithm = best_algorithms[0]\n",
    "    best_score = auroc_mean[auroc_mean.algorithm == best_algorithms[0]].ROC_AUC.tolist()[0]\n",
    "    best_algorithm = algo_meta[best_algorithm][\"display_name\"]\n",
    "    return f\"{best_algorithm}\\n({best_score:.4f})\"\n",
    "\n",
    "\n",
    "def fill_plot_data(name, types, df, filters, scatter_plots, best_per_type, types_list):\n",
    "    local_means = []\n",
    "    for i, type_ in enumerate(types):\n",
    "        grouped = df[filters(type_)].groupby(\"algorithm\")[\"ROC_AUC\"]\n",
    "        auroc = grouped.mean().reset_index()\n",
    "        local_means.append(auroc.ROC_AUC.mean())\n",
    "    order = np.argsort(local_means)\n",
    "    \n",
    "    for i, type_ in enumerate(np.array(types)[order].tolist()):\n",
    "        grouped = df[filters(type_)].groupby(\"algorithm\")\n",
    "        auroc = grouped[\"ROC_AUC\"].mean().reset_index()\n",
    "        type_name = f\"{type_}{name}\"\n",
    "        types_list.append(type_name)\n",
    "        best_per_type.append(get_best_algorithm(grouped))\n",
    "        means.append(auroc.ROC_AUC.mean())\n",
    "        auroc = auroc[auroc.ROC_AUC >= x_range[0] - 0.05]\n",
    "        for _, row in auroc.iterrows():\n",
    "            method_family = algo_meta[row.algorithm][\"method_family\"]\n",
    "            scatter_plots[method_family][\"xs\"].append(row.ROC_AUC)\n",
    "            scatter_plots[method_family][\"ys\"].append(len(types_list) + method_family_y(name=method_family))\n",
    "            scatter_plots[method_family][\"cs\"].append(colormap(method_families.index(method_family)))\n",
    "    return scatter_plots, best_per_type, types_list\n",
    "\n",
    "\n",
    "# dimensionality\n",
    "scatter_plots, best_per_type, types = fill_plot_data(\"\", dimensions, df_gt, lambda t: df_gt.dataset_input_dimensionality.str.lower() == t, scatter_plots, best_per_type, types)\n",
    "hlines.append(len(types))\n",
    "\n",
    "# periodicity\n",
    "periodicity_types = [\"periodic\", \"non-periodic\"]\n",
    "scatter_plots, best_per_type, types = fill_plot_data(\"\", periodicity_types, df_gt, lambda t: df_gt.dataset.apply(lambda x: x.split(\"-\")[0]).isin(periodicity_lists[periodicity_types.index(t)]), scatter_plots, best_per_type, types)\n",
    "hlines.append(len(types))\n",
    "\n",
    "# trends\n",
    "scatter_plots, best_per_type, types = fill_plot_data(\" trend\", trends, df_tr, lambda t: df_tr.trend == t , scatter_plots, best_per_type, types)\n",
    "hlines.append(len(types))\n",
    "\n",
    "# base oscillations\n",
    "scatter_plots, best_per_type, types = fill_plot_data(\"\", base_oscillations, df_bo, lambda t: df_bo.dataset.str.startswith(t), scatter_plots, best_per_type, types)\n",
    "hlines.append(len(types))\n",
    "\n",
    "# anomaly types\n",
    "scatter_plots, best_per_type, types = fill_plot_data(\"\", anomaly_types, df_an, lambda t: df_an.dataset.str.contains(t), scatter_plots, best_per_type, types)\n",
    "hlines.append(len(types))\n",
    "\n",
    "y_lut = lambda type_: types.index(type_)\n",
    "    \n",
    "for family, scatter in scatter_plots.items():\n",
    "    xs = scatter[\"xs\"]\n",
    "    ys = scatter[\"ys\"]\n",
    "    cs = scatter[\"cs\"]\n",
    "    ax.scatter(xs, ys, edgecolors=cs, s=20, facecolors='none', marker=method_family_marker_map(family))\n",
    "ax.legend(handles=[\n",
    "    Line2D([0], [0], marker=method_family_marker_map(fam), color='w', label=fam, markerfacecolor=\"none\", markeredgecolor=colormap(i), markersize=8)\n",
    "    for i, fam in enumerate(method_families)\n",
    "] + [\n",
    "    Line2D([0], [0], label=\"mean\", color=\"black\", linewidth=1)\n",
    "], loc=\"lower right\", ncol=4, bbox_to_anchor=[1+xlim_ext_r, 1.01])#[0.4, 1.01 + 1./len(types)])\n",
    "\n",
    "annotation_x = 1.04\n",
    "\n",
    "ax.annotate(\"Best algorithm:\", (annotation_x, len(types)-0.45), annotation_clip=False)\n",
    "for type_, best in zip(types, best_per_type):\n",
    "    ax.annotate(best.replace(\"Normalizing\", \"Norm.\"), (annotation_x, y_lut(type_)), annotation_clip=False, fontsize=fontsize, va=\"center\")\n",
    "\n",
    "ax.annotate(\"Characteristic:\", (0.35, len(types)-0.45), annotation_clip=False, ha=\"right\")\n",
    "last = 0\n",
    "for hline, characteristic in zip(hlines, [\"dimensionality\", \"periodicity\", \"trend\", \"base oscillation\", \"anomaly type\"]):\n",
    "    ax.annotate(characteristic, (x_range[0]-xlim_ext_l+0.005, hline-0.55), rotation=90, fontsize=fontsize, va=\"top\")\n",
    "    last = hline\n",
    "    if characteristic != \"anomaly type\":\n",
    "        ax.hlines(hline - 0.5, x_range[0]-xlim_ext_l, x_range[1]+xlim_ext_r, color=\"black\", linewidth=1, linestyle=\"--\", clip_on=False)\n",
    "    \n",
    "    \n",
    "for mean, (i, _) in zip(means, enumerate(types)):\n",
    "    ax.vlines(mean, i-0.5, i+0.5, color=\"black\", linewidth=1, alpha=0.7)\n",
    "    if len(means) > i+1 and i+1 not in hlines:\n",
    "        next_mean = means[i+1]\n",
    "        ax.hlines(i+0.5, min(mean, next_mean), max(mean, next_mean), color=\"black\", linewidth=1, alpha=0.7)\n",
    "\n",
    "plt.yticks(*zip(*list(enumerate(map(lambda x: x.replace(\" trend\", \"\").replace(\"variate\", \"-\"), types)))), rotation=45)\n",
    "plt.xlabel(\"AUC-ROC\")\n",
    "#plt.ylabel(\"Dataset characteristic\")\n",
    "plt.xlim(x_range[0]-xlim_ext_l, x_range[1]+xlim_ext_r)\n",
    "plt.ylim(-0.5, len(types)-0.5)\n",
    "plt.savefig(\"plots/data-characteristic-scatter-heat.pdf\", bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dimensions Influence (not for eval paper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [],
   "source": [
    "colormap = method_family_colormap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_dim = df_gt.copy()\n",
    "uni_datasets = df_dim[df_dim.dataset_input_dimensionality == \"UNIVARIATE\"].dataset.tolist()\n",
    "multi_datasets = df_dim[df_dim.dataset_input_dimensionality == \"MULTIVARIATE\"].dataset.tolist()\n",
    "df_dim.loc[df_dim.dataset.isin(uni_datasets), \"ndims\"] = 1\n",
    "df_dim.loc[df_dim.dataset.isin(multi_datasets), \"ndims\"] = df_dim[df_dim.dataset.isin(multi_datasets)].dataset.apply(lambda x: int(x.split(\"-\")[-1]))\n",
    "ndimensions = df_dim.ndims.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x_range = (0.4, 1.0)\n",
    "\n",
    "xs = []\n",
    "ys = []\n",
    "cs = []\n",
    "\n",
    "types = []\n",
    "best_per_type = []\n",
    "hlines = []\n",
    "means = []\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "ax = fig.gca()\n",
    "\n",
    "def get_best_algorithm(grouped: pd.DataFrame) -> str:\n",
    "    auroc_mean = grouped[\"ROC_AUC\"].mean().reset_index()\n",
    "    max_auroc = auroc_mean.ROC_AUC.max()\n",
    "    \n",
    "    best_algorithms = auroc_mean[auroc_mean.ROC_AUC == max_auroc].algorithm.tolist()\n",
    "    \n",
    "    if len(best_algorithms) > 1:\n",
    "        # lowest std\n",
    "        auroc_std = grouped[\"ROC_AUC\"].std().reset_index()\n",
    "        auroc_std = auroc_std[(auroc_std.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = auroc_std[(auroc_std.ROC_AUC == auroc_std.ROC_AUC.min())].algorithm.tolist()\n",
    "        \n",
    "        # fewest errors\n",
    "        auroc_count = grouped[\"ROC_AUC\"].count().reset_index()\n",
    "        auroc_count = auroc_count[(auroc_count.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = auroc_count[(auroc_count.ROC_AUC == auroc_count.ROC_AUC.max())].algorithm.tolist()\n",
    "        \n",
    "        # fastest runtime\n",
    "        mean_time = grouped[\"overall_time\"].mean().reset_index()\n",
    "        mean_time = mean_time[(mean_time.algorithm.isin(best_algorithms))]\n",
    "        best_algorithms = mean_time[(mean_time.overall_time == mean_time.overall_time.min())].algorithm.tolist()\n",
    "            \n",
    "    return f\"{best_algorithms[0]} ({auroc_mean[auroc_mean.algorithm == best_algorithms[0]].ROC_AUC.tolist()[0]:.2f})\"\n",
    "\n",
    "\n",
    "def fill_plot_data(name, types, df, filters, xs, ys, cs, best_per_type, types_list):\n",
    "    local_means = []\n",
    "    for i, type_ in enumerate(types):\n",
    "        grouped = df[filters(type_)].groupby(\"algorithm\")[\"ROC_AUC\"]\n",
    "        auroc = grouped.mean().reset_index()\n",
    "        local_means.append(auroc.ROC_AUC.mean())\n",
    "    order = np.argsort(local_means)\n",
    "    \n",
    "    for i, type_ in enumerate(np.array(types)[order].tolist()):\n",
    "        grouped = df[filters(type_)].groupby(\"algorithm\")\n",
    "        auroc = grouped[\"ROC_AUC\"].mean().reset_index()\n",
    "        type_name = f\"{type_}{name}\"\n",
    "        types_list.append(type_name)\n",
    "        best_per_type.append(get_best_algorithm(grouped))\n",
    "        means.append(auroc.ROC_AUC.mean())\n",
    "        auroc = auroc[auroc.ROC_AUC >= x_range[0] - 0.05]\n",
    "        xs.extend(auroc.ROC_AUC)\n",
    "        ys.extend([len(types_list) + method_family_y(name=algo_meta[x][\"method_family\"]) for _, x in auroc.algorithm.iteritems()])\n",
    "        cs.extend([colormap(method_families.index(algo_meta[x][\"method_family\"])) for _, x in auroc.algorithm.iteritems()])\n",
    "    return xs, ys, cs, best_per_type, types_list\n",
    "\n",
    "\n",
    "# dimensionality\n",
    "xs, ys, cs, best_per_type, types = fill_plot_data(\"\", ndimensions, df_dim, lambda t: df_dim.ndims == t, xs, ys, cs, best_per_type, types)\n",
    "\n",
    "y_lut = lambda type_: types.index(type_)\n",
    "    \n",
    "ax.scatter(xs, ys, edgecolors=cs, s=20, facecolors='none')\n",
    "ax.legend(handles=[\n",
    "    Line2D([0], [0], marker='o', color='w', label=fam, markerfacecolor=\"none\", markeredgecolor=colormap(i), markersize=8)\n",
    "    for i, fam in enumerate(method_families)\n",
    "] + [\n",
    "    Line2D([0], [0], label=\"mean\", color=\"black\", linewidth=1)\n",
    "], loc=\"upper center\", ncol=4, bbox_to_anchor=[0.5, 1.01 + 1./len(types)])\n",
    "\n",
    "annotation_x = 1.07\n",
    "\n",
    "ax.annotate(\"Best algorithms:\", (annotation_x, len(types)), annotation_clip=False)\n",
    "for type_, best in zip(types, best_per_type):\n",
    "    ax.annotate(best, (annotation_x, y_lut(type_)), annotation_clip=False, fontsize=14, va=\"center\")\n",
    "    \n",
    "    \n",
    "for mean, (i, _) in zip(means, enumerate(types)):\n",
    "    ax.vlines(mean, i-0.5, i+0.5, color=\"black\", linewidth=1, alpha=0.7)\n",
    "    if len(means) > i+1 and i+1 not in hlines:\n",
    "        next_mean = means[i+1]\n",
    "        ax.hlines(i+0.5, min(mean, next_mean), max(mean, next_mean), color=\"black\", linewidth=1, alpha=0.7)\n",
    "\n",
    "plt.yticks(*zip(*list(enumerate(types))))\n",
    "plt.xlabel(\"ROC_AUC\")\n",
    "plt.ylabel(\"Time series dimensionality\")\n",
    "plt.xlim(x_range[0]-0.05, x_range[1]+0.05)\n",
    "plt.ylim(-0.5, len(types)-0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Runtime Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def colormap(i: int, train: bool):\n",
    "    if not train:\n",
    "        i *= 2\n",
    "    else:\n",
    "        i = i * 2 + 1\n",
    "    return learning_type_colormap(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "gutentag_length = pd.read_csv(gutentag_data_path / \"datasets.csv\")[[\"collection_name\", \"dataset_name\", \"length\"]]\n",
    "gutentag_length.loc[:, \"dataset_name\"] = gutentag_length.dataset_name.str.split(\".\").apply(lambda x: x[0])\n",
    "gutentag_length = gutentag_length.drop_duplicates()\n",
    "\n",
    "benchmark_length = pd.read_csv(benchmark_data_path / \"datasets.csv\")[[\"collection_name\", \"dataset_name\", \"length\"]]\n",
    "df_len = pd.concat([gutentag_length, benchmark_length], ignore_index=True).set_index([\"collection_name\", \"dataset_name\"]).length\n",
    "\n",
    "# all univariate and multivariate algorithms on as many univariate datasets as possible\n",
    "df_uni = df[df.dataset_input_dimensionality == \"UNIVARIATE\"].copy()\n",
    "df_uni.loc[:, \"dataset_length\"] = df_uni[[\"collection\", \"dataset\"]].apply(lambda row: df_len[(row.collection, row.dataset)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use time limit as runtime for timed-out runs and also mark it with an icon at the algorithm name\n",
    "TIMEOUT = 4 * 3600\n",
    "ALLOWED_STATI = [f\"Status.{s.upper()}\" for s in [\"timeout\", \"ok\"]]\n",
    "df_uni.loc[(df_uni.status == \"Status.TIMEOUT\") & (df_uni.algo_training_type != \"UNSUPERVISED\"), \"overall_time\"] = TIMEOUT\n",
    "df_uni.loc[(df_uni.status == \"Status.TIMEOUT\") & (df_uni.algo_training_type == \"UNSUPERVISED\"), \"overall_time\"] = TIMEOUT / 2\n",
    "df_uni.loc[(df_uni.status == \"Status.TIMEOUT\") & (df_uni.algo_training_type != \"UNSUPERVISED\"), \"train_main_time\"] = TIMEOUT / 2\n",
    "df_uni.loc[(df_uni.algo_training_type == \"UNSUPERVISED\"), \"train_main_time\"] = 0\n",
    "df_uni.loc[df_uni.status == \"Status.TIMEOUT\", \"execute_main_time\"] = TIMEOUT / 2\n",
    "df_uni = df_uni[df_uni.status.isin(ALLOWED_STATI)]\n",
    "\n",
    "timed_out_algos = df_uni[df_uni.status == \"Status.TIMEOUT\"].algorithm.unique()\n",
    "#df_uni.loc[:, \"display_name\"] = df_uni.algorithm.apply(lambda a: f\"{algo_short_name_lut.get(a, a.replace(' (RR)', ''))} †\" if a in timed_out_algos else f\"{algo_short_name_lut.get(a, a.replace(' (RR)', ''))}  \")\n",
    "mask = df_uni[\"algorithm\"].isin(timed_out_algos)\n",
    "df_uni.loc[mask, \"algo_display_name\"] = df_uni.loc[mask, \"algo_display_name\"] + \" †\"\n",
    "\n",
    "# try to normalize runtime by dataset length\n",
    "df_uni.loc[:, \"normalized_train_time\"] = df_uni.train_main_time / df_uni.dataset_length\n",
    "df_uni.loc[:, \"normalized_execute_time\"] = df_uni.execute_main_time / df_uni.dataset_length\n",
    "df_uni.loc[:, \"normalized_overall_time\"] = df_uni.normalized_train_time + df_uni.normalized_execute_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MULTIVARIATE algorithms take on average 0.127 seconds per data point.\n",
      "UNIVARIATE algorithms take on average 0.044 seconds per data point.\n",
      "\n",
      "SUPERVISED algorithms take on average 0.404 seconds per data point.\n",
      "SEMI_SUPERVISED algorithms take on average 0.248 seconds per data point.\n",
      "UNSUPERVISED algorithms take on average 0.029 seconds per data point.\n",
      "Trained algorithms take on average 0.255 seconds per data point.\n",
      "\n",
      "XGBoosting (RR), RobustPCA, TARZAN, LaserDBN are faster than 2.439 milliseconds.\n",
      "XGBoosting (RR), RobustPCA, TARZAN, LaserDBN are faster than 68% of ['UNSUPERVISED'] algorithms, which take 40.69 milliseconds.\n",
      "NormA, Triple ES (Holt-Winter's), HOT SAX, DSPOT, ARIMA are slower than 125.199 milliseconds.\n",
      "NormA, Triple ES (Holt-Winter's), HOT SAX, DSPOT, ARIMA are slower than 55% of ['SUPERVISED', 'SEMI_SUPERVISED'] algorithms, which take 37.73 milliseconds.\n",
      "\n",
      "TSBitmap takes 927.01 microseconds with a ROC_AUC score of 0.5881031348953089.\n",
      "Normalizing Flows takes 0.95 seconds with a ROC_AUC score of 0.8684010130814523.\n",
      "DWT-MLEAD takes 2.23 milliseconds with a ROC_AUC score of 0.8290607358486228.\n",
      "STOMP takes 3.13 milliseconds with a ROC_AUC score of 0.7358578874791515.\n"
     ]
    }
   ],
   "source": [
    "second = 1\n",
    "milli = 1000\n",
    "micro = milli * 1000\n",
    "nano = micro * 1000\n",
    "\n",
    "unit_names = {\n",
    "    second: \"seconds\",\n",
    "    milli: \"milliseconds\",\n",
    "    micro: \"microseconds\",\n",
    "    nano: \"nanoseconds\"\n",
    "}\n",
    "\n",
    "\n",
    "def dim_mean_time(dim):\n",
    "    t = df_uni.loc[df_uni.algo_input_dimensionality == dim, \"normalized_overall_time\"].mean()\n",
    "    print(f\"{dim} algorithms take on average {t:.3f} seconds per data point.\")\n",
    "\n",
    "dim_mean_time(\"MULTIVARIATE\")\n",
    "dim_mean_time(\"UNIVARIATE\")\n",
    "\n",
    "def tt_mean_time(training_type):\n",
    "    t = df_uni.loc[df_uni.algo_training_type == training_type, \"normalized_overall_time\"].mean()\n",
    "    print(f\"{training_type} algorithms take on average {t:.3f} seconds per data point.\")\n",
    "\n",
    "print()\n",
    "tt_mean_time(\"SUPERVISED\")\n",
    "tt_mean_time(\"SEMI_SUPERVISED\")\n",
    "tt_mean_time(\"UNSUPERVISED\")\n",
    "\n",
    "t = df_uni.loc[df_uni.algo_training_type.isin([\"SUPERVISED\", \"SEMI_SUPERVISED\"]), \"normalized_overall_time\"].mean()\n",
    "print(f\"Trained algorithms take on average {t:.3f} seconds per data point.\")\n",
    "\n",
    "def these_algos_are_faster_than(algos, faster=True):\n",
    "    t = df_uni.groupby(\"algorithm\").mean().loc[algos, \"normalized_overall_time\"]\n",
    "    if faster:\n",
    "        t = t.max() * 1000\n",
    "    else:\n",
    "        t = t.min() * 1000\n",
    "    print(f\"{', '.join(algos)} are {'faster' if faster else 'slower'} than {t:.3f} milliseconds.\")\n",
    "\n",
    "def these_algos_are_faster_than_pct_learning_type(algos, learning_type, faster=True):\n",
    "    learning_type = [learning_type] if type(learning_type) == str else learning_type\n",
    "    t = df_uni.groupby(\"algorithm\").mean().loc[algos, \"normalized_overall_time\"]\n",
    "    tmp = df_uni[df_uni.algo_training_type.isin(learning_type)].groupby(\"algorithm\").mean()\n",
    "    if faster:\n",
    "        others = tmp[tmp.normalized_overall_time > t.max()]\n",
    "        pct = others.shape[0] / tmp.shape[0]\n",
    "    else:\n",
    "        others = tmp[tmp.normalized_overall_time < t.min()]\n",
    "        pct = others.shape[0] / tmp.shape[0]\n",
    "    others_t = others.mean().normalized_overall_time * 1000\n",
    "    print(f\"{', '.join(algos)} are {'faster' if faster else 'slower'} than {pct:.0%} of {learning_type} algorithms, which take {others_t:.2f} milliseconds.\")\n",
    "\n",
    "print()\n",
    "these_algos_are_faster_than([\"XGBoosting (RR)\", \"RobustPCA\", \"TARZAN\", \"LaserDBN\"])\n",
    "these_algos_are_faster_than_pct_learning_type([\"XGBoosting (RR)\", \"RobustPCA\", \"TARZAN\", \"LaserDBN\"], \"UNSUPERVISED\")\n",
    "these_algos_are_faster_than([\"NormA\", \"Triple ES (Holt-Winter's)\", \"HOT SAX\", \"DSPOT\", \"ARIMA\"], False)\n",
    "tmp = these_algos_are_faster_than_pct_learning_type([\"NormA\", \"Triple ES (Holt-Winter's)\", \"HOT SAX\", \"DSPOT\", \"ARIMA\"], [\"SUPERVISED\", \"SEMI_SUPERVISED\"], False)\n",
    "\n",
    "def algo_time(algo, unit):\n",
    "    df_algo = df_uni[df_uni.algorithm == algo][[\"normalized_overall_time\", \"ROC_AUC\"]].mean()\n",
    "    t = df_algo.normalized_overall_time * unit\n",
    "    auroc = df_algo.ROC_AUC\n",
    "    print(f\"{algo} takes {t:.2f} {unit_names[unit]} with a ROC_AUC score of {auroc}.\")\n",
    "\n",
    "print()\n",
    "algo_time(\"TSBitmap\", micro)\n",
    "algo_time(\"Normalizing Flows\", second)\n",
    "algo_time(\"DWT-MLEAD\", milli)\n",
    "algo_time(\"STOMP\", milli)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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3n/vuu4+pU6eyc+dOtm/fzlNPPUVaWhotW7akTp06NGrUiOHDh7N7925effVVFi5cmOf/q1ev5umnnyYrK4t3332XOXPm0LVrVxo0aECXLl24+eab2bRpE9nZ2SxcuJBvvvmm0PL06tWLt99+mzfffDNfsz+Ij+pXX33Fjh07qFy5MsnJySQmJgJw1VVXMXDgwBw3iTVr1vDRRx8BcM455/DJJ58wadIkdu7cyV133UV2dnast1BRFEVRlCgpV4KqtXa0tfYKT0gJlXY94aZZMHiD/IYopAKkpqYyZcoUjjjiCFJSUujUqRNt2rRh6NChnHnmmdx+++2cf/75VKtWjTZt2hTJb7QoGGO49NJLqV27Ng0bNuTzzz9nzJgxVK1aFYCXXnqJRx99lFq1ajF79uw9tLpHHHEE8+fPp3bt2gwcOJD33nsvx5z++uuvs3PnTg4++GBq1KjBOeecw19//VVoebz7sWLFihz3hEh27NhB//79qV27NvXr12f16tUMGTIEgH79+tGjRw+6dOlCamoqnTp1YsqUKQC0bt2a//73v/Tq1YsGDRpQo0aNQiMiKIqiKIoSLsZvht3XMcZ0B7pXqlSp7/bt2/c4PmfOHFq1alX8BSsnZGZm8vLLLzNp0qSSLoqi7BWtDxRFUWLHGLPNWpsS9P+qUVUURVEURVFKJeVKUFUURVEURVHKDuVKUI3bYColKvr06aNmf0VRFEVRoqZcCapq+lcURVEURSk7VCjpAhQnvsFUJV0URVEURVEUZS+oRlVRFEVRFEUplZQrQVVRFEVRFEUpO6jpX1EURVEURSmVlCuNqpr+489VV13FfffdF2qaEyZM0BmhFEVRFKUcUq4E1bLOpEmTOOqoo6hevTo1a9akc+fOTJ06taSLlYcXXniBQYMGlXQxFEVRFEXZByhXpv94MmbRGJ76+SlWbl1J/ZT69DukH6c1Oy209Ddt2kS3bt14/vnn6dmzJzt37mTixIkUpxuDtRZrLQkJ2r9RFEVRFCX+lCuJI14B/8csGsPg7wfz19a/sFj+2voXg78fzJhFY0LLY968eQBccMEFJCYmkpycTJcuXWjXrh2DBw/mwgsvzDn3zz//xBjDrl27AMjIyOCOO+6gY8eOVK9endNPP51169blnD958mSOOuoo0tLSaN++PRMmTMg5lpGRwcCBA+ncuTNVqlRhyJAhHHbYYXnK9sQTT9CjRw9AgvrfeeedAKxdu5Zu3bqRlpZGzZo1OeaYY8jOzgZgxYoVnH322dSpU4emTZvy9NNP56T3zz//0KdPH2rUqMHBBx9c6rTGiqIoiqIUD+VKUI2Xj+pTPz/F9t3b8+zbvns7T/38VGh5HHjggSQmJnLJJZcwduxY1q9fX6T/v/7667z66qusWLGCChUqcMMNNwCwfPlyTjvtNO68807WrVvHY489xtlnn82aNWty/vvGG28wbNgwNm/ezPXXX8/cuXOZP39+zvERI0bQq1evPfIcOnQo++23H2vWrGHVqlUMGTIEYwzZ2dl0796d9u3bs3z5cr788kuefPJJxo0bB8A999zDwoULWbhwIePGjeO1114LcssURVEURSnjlCtBNV6s3LqySPuDUK1aNSZNmoQxhr59+1KnTh169OjBqlWrovr/RRddRJs2bUhJSeG+++7jnXfeYffu3QwfPpyuXbvStWtXEhISOOmkkzjssMP49NNPc/7bp08fWrduTYUKFXI0siNHjgRg/vz5/P777zkaVT9JSUn89ddfLF68mKSkJI455hiMMUydOpU1a9Zw1113UbFiRZo1a0bfvn156623AHjnnXcYOHAgNWvWJD09PUeoVhRFURSlfKGCagjUT6lfpP1BadWqFZmZmSxbtoxZs2axYsUKbrzxxqj+m56enrPeuHFjsrKyWLt2LYsXL+bdd98lLS0tZ5k0aRJ//fVXvv8F6NWrV46gOmLECM444wyqVKmyR5633norLVq0oEuXLjRr1oyHHnoIgMWLF7NixYo8eQ4ZMiRH6F6xYsUe5VUURVEUpfxRrgTVePmo9jukH5UTK+fZVzmxMv0O6RdqPn4OOugg+vTpw6xZs0hJSWHbtm05x1au3FOTu3Tp0pz1JUuWkJSURO3atUlPT+eiiy5iw4YNOcvWrVvp379/zvnGmDxpdenShbVr1zJjxgxGjhyZr9kfIDU1laFDh7Jo0SJGjx7N448/zpdffkl6ejpNmzbNk+fmzZtztLgNGjTYo7yKoiiKopQ/ypWgGi8f1dOancbgowbTIKUBBkODlAYMPmpwqKP+f//9d4YOHcqyZcsAETxHjhxJp06d6NChA99++y1Llixh48aNPPjgg3v8f/jw4fz2229s27aNu+66i3POOYfExEQuvPBCRo8ezbhx49i9ezfbt29nwoQJOfnkR4UKFTjnnHO49dZbWbduHSeddFK+533yyScsWLAAay3VqlUjMTGRxMREOnbsSLVq1Xj44Yf5559/2L17N7NmzcoZNNWzZ08efPBB1q9fz7Jly3jmmWdCuIOKoiiKopQ1ypWgGk9Oa3Ya488Zz8xLZjL+nPGhCqkg2skpU6ZwxBFHkJKSQqdOnWjTpg1Dhw7lpJNO4rzzzqNdu3YceuihdOvWbY//X3TRRfTp04f69euzffv2nFH26enpfPTRRwwZMoQ6deqQnp7Oo48+mjM6vyB69erFF198wbnnnkuFCvlHOZs/fz4nnngiVatW5cgjj+Saa64hIyODxMRERo8ezYwZM2jatCm1a9fm8ssvZ+PGjQDcfffdNG7cmKZNm9KlSxcuuuiiGO+eoiiKoihlEWOtLekyFDspKSl269ate+yfM2cOrVq1KoESxZeMjAwuvPBCLr/88pIuiqKUGfbV+kBRFKU4McZss9amBP2/alQVRVEURVGUUokKqoqiKIqiKEqpRKdQLQf4Z5pSFEVRFEUpK5QrQdUY0x3oXqlSpZIuiqIoiqIoirIXypXpP17hqRRFURRFUZTwKVeCqqIoiqIoilJ2UEFVURRFURRFKZWooKooiqIoiqKUSlRQLSO0bt065tH7mZmZHH300YH+++eff2KMYdeuXTGVYW+8+eabdOnSJa55KIqiKIpSNlBBNUQGDx7M4MGD45L27NmzycjIiEvaJUV+wm/v3r0ZP358CZYqf4wxLFiwoKSLoSiKoijlChVUFUVRFEVRlFJJqRRUjTHXGWN+MsbsMMZklnR59kafPn2oWrUqQ4YMYciQIVStWpU+ffqEmkeTJk344osvANHc9uzZk4svvpjU1FRat27NTz/9lHPu0qVLOeuss6hTpw61atXiuuuu2yO9/LSZGRkZvPzyywDs3r2bW265hdq1a9OsWTPGjBmT5/8bN27ksssuo0GDBjRq1Ig777yT3bt3A7BgwQKOO+44qlevTu3atTnvvPPyvaZjjz0WgLS0NKpWrcoPP/ywh3uCMYbnnnuOAw44gNTUVAYNGsTChQs58sgjqVatGj179mTnzp0553/yySd06NCBtLQ0jjrqKGbOnJlzbM6cOWRkZJCWlkbr1q35+OOP8712yOsm4ZWzffv2VK1albfffpu1a9fSrVs30tLSqFmzJscccwzZ2dn5XqeiKIqiKMEolYIqsAK4H3i1pAsSDZmZmWzZsoUBAwYwYMAAtmzZQmZmZlzz/Pjjjzn//PPZsGEDPXr0yBFGd+/eTbdu3WjcuDF//vkny5cv5/zzzy9y+i+99BKffPIJ06dP56effuK9997Lc/ySSy6hQoUKLFiwgOnTpzN+/PgcQW/QoEF06dKF9evXs2zZMq6//vp88/j2228B2LBhA1u2bOHII4/M97zPPvuMadOmMXnyZB555BGuuOIK3nzzTZYuXcqsWbMYOXIkAD///DP//ve/efHFF/n777+58sor6dGjBzt27CArK4vu3bvTpUsXVq9ezTPPPEPv3r2ZO3fuXu+FV85ffvmFLVu2cN555zF06FD2228/1qxZw6pVqxgyZAjGmOhurqIoiqIoUVEqBVVr7Shr7YfA33s71xjTwhjzjTFmozFmrTHm7fiXsOQ5+uij6dq1K4mJiVx00UX88ssvAPz444+sWLGCRx99lJSUFCpXrhxoANU777zDjTfeSHp6OjVr1uSOO+7IObZq1SrGjh3Lk08+SUpKCnXr1uWmm27irbfeAiApKYnFixezYsWKwPn7uf3226lWrRqtW7emTZs2dOnShWbNmlG9enVOPfVUpk+fDohwfeWVV3LEEUeQmJjIJZdcQqVKlZg8eTKTJ09my5Yt9O/fn4oVK3LCCSfQrVu3HCG3qCQlJfHXX3+xePFikpKSOOaYY1RQVRRFUZSQKZWCahG5DxgP1AD2A57J7yRjzBXOneCneI9cLw7q16+fs16lShW2b9/Orl27WLp0KY0bN6ZChdhmx12xYgXp6ek5240bN85ZX7x4MVlZWTRo0IC0tDTS0tK48sorWb16NQCPPPII1lo6duxI69atefXV2BTj9erVy1lPTk7eY3vLli055Ro6dGhOmdLS0li6dCkrVqzIuZ6EhNxXvnHjxixfvjxQmW699VZatGiRIzQ/9NBDAa9OURRFUZSCiE2aKR1kAY2BhtbaZcCk/E6y1g4DhgGkpKTY4ite8ZKens6SJUvYtWtXocJqSkoKANu2baNatWoArFy5Mud4gwYNWLp0ac72kiVL8uRRqVIl1q5dm28e9evX56WXXgJg0qRJnHjiiRx77LG0aNEiz3lhayDT09MZOHAgAwcO3OPYxIkTWbp0KdnZ2TnC6pIlSzjwwAMBuR/btm3LOd9/L/IjNTWVoUOHMnToUGbPns3xxx/P4Ycfzv/93/+FeEWKoihKaabta21DTe/XS37dc+fg6qHmweCN4aYXZ/YFjeptgAF+NMbMNsb8u6ATjTHdjTHDvEE/+yIdO3akQYMG9O/fn61bt7J9+3a+++67Pc6rU6cOjRo1Yvjw4ezevZtXX32VhQsX5hzv2bMnTz/9NMuWLWP9+vV5NIYNGjSgS5cu3HzzzWzatIns7GwWLlzIN998A8C7777LsmXLAKhRowbGGBITE/MtQ0JCAosWLQrl2vv27csLL7zAlClTsNaydetWxowZw+bNmzniiCNISUnhkUceISsriwkTJjB69Ogc/90OHTowatQotm3bxoIFC3jllVfypF2vXr085fzkk09YsGAB1lqqVatGYmJivteoKIqiKEpwyrxG1Vq7EugLYIw5GvjCGPOttXaPoJfW2tHA6JSUlL7FXMxiIzExkdGjR3PDDTew//77Y4yhV69edO7ceY9zX3rpJa655hoGDBjAZZddxlFHHZVzrG/fvsybN4/27dtTrVo1brnlFr766quc46+//jr9+/fn4IMPZvPmzTRr1ozbb78dgKlTp3LjjTeyceNG6tWrx1NPPUXTpk33yL9KlSoMHDiQzp07k5WVxWeffRbTtR922GG89NJLXHfddcyfP5/k5GSOPvpojj32WCpWrMjHH3/MNddcw4MPPkijRo14/fXXOeiggwC46aabmDp1KvXq1aNdu3b07t07J8oCSKSFSy65hH/++Ydhw4axfPlyrrvuOtasWUONGjW45ppr9rk4t4qiKGWacq6J3Fcw1u7dCm6M+chae3o++0dZa88KvVDGVECE6LsRv9O+wC5r7R7OpcaYc4EfrLXLjDGtgZ+Ag621f+Rzbnege6VKlfpu3759j3znzJlDq1atApd7yJAhAAwYMCBwGoqilA5irQ8URSmAsAVIyF+ILAZBVU3/e8cYs81amxL0/9FqVI8vYH9G0Iz3wp2IkOpxIXAPMDifcw8HnjTGVAdWAf3yE1Ih/hpVFVAVRVEURVHCo1BB1Rhzr1ut6Fv3aAYsjkehrLWDyV8oze/c2xA/1b3i06gGLpuiKIqiKIpSPOxNo+rFJ0rwrQNYYClRCpOlhfLgo6ooiqIogSnjZmZl36NQQdVaeymAMeZ7a+1LxVMkRVEURVEURYnSR9Va+5LzAW0JVI049lX+/yp9qOlfURRFKbOotlMph0QlqBpj+gD/BbYA23yHLOKrWiZQ07+iKIqiKErZIdqA/w8A51hr61lrm/qWMiOkKoqiKEpZJiNzK5kzdgKQtduSkbmV4TNle1uWbL89KwuAjdtle9Qc2V67LZuMzK2MnivbK7dkl8AVKErRiTY8VQVgfDwLUhyo6V9RFEVRFKXsEK1G9WHgTmNMmZ5y1Vo72lp7RXmY6jIjI4OXX3450H+XLFlC1apVKY1TzZbmsoWFMYYFC2Ritauuuor77ruvSP8fP348Z5xxRmjplRUGDx7MhRdeGNW5Z511VswzoSlKcTOhTwp9OlQEICnRMKFPChe2k+0qSbJ9XpskAKpXlu2zWsl27SoJTOiTQveWsl2/apluzpVyRLRv6k1IEP7Nxpgl/iWOZVOKiSZNmuSZLnT//fdny5Ytoc9dn5mZydFHHx1TGkHKNmLECHr16sWff/6JMYZdu/aY4KzI9OnThzvvvDPmdPbGCy+8wKBBgwCYMGEC++23317/M2DAAPr377/X9Moz/fv3Z+DAgSVdDEVRFGUvRGv6j05NUcrZV0z/u3btokKFaB9d2WL37t2hC8iffvopXbt2DTXN0srUqVPZuHEjnTp1Kumi5GCtxVpLQkLp0eB07NiRTZs28dNPP3HYYYeVdHEURVGUAoiq5bDWflPQEu8ChklZNv03adKEhx9+mHbt2pGSksKuXbuYPHkyRx11FGlpabRv354JEybk+9+FCxdywgknUKtWLWrXrk3v3r3ZsGEDABdddBFLliyhe/fuVK1alUceeSSP5vGtt97aoyF/4okn6NGjBwA7duzglltuYf/996devXpcddVV/PPPP3uUYc6cOVx11VX88MMPVK1albS0NEA0k1dffTVdu3YlJSWFr7/+mjFjxvCvf/2LatWqkZ6ezuDBg3PSidSKZmRkMGjQIDp37kxqaipdunRh7dq1OednZ2fz+eefc8opp3DssccCkJaWRtWqVfnhhx8AePXVV2nVqhU1atTg5JNPZvFimXDNWstNN91E3bp1qV69Ou3atWPWrFkMGzaMN998k0ceeYSqVavSvXv3Pa63oP9613zVVVdx0kknkZqaynHHHZeTZySe5nbr1q2ceuqprFixgqpVq1K1alVWrFixx/ljx47luOOOyzctf3qQq6EdOnQodevWpUGDBvzvf//LObewZ7t+/Xq6detGnTp1qFGjBt26dWPZsmU5/83IyGDgwIF07tyZKlWqsGjRoj3K8vDDD9OoUSNSU1Np2bIlX375JSCdlSFDhtC8eXNSU1M59NBDWbp0KQD9+vUjPT2datWqceihhzJx4sQCr3Vv30dGRgZjxowp8P+KoihKyVOgoGqMGehbv7egpXiKWXJkZGSQmZkJQFZWFhkZGQwfPhyAbdu2kZGRwdtvvw3Axo0bycjIYNSoUQCsXbuWjIwMRo8eDcDKlSvJyMjI8Y3zGt9oGTlyJGPGjGHDhg2sWrWK0047jTvvvJN169bx2GOPcfbZZ7NmzZo9/met5Y477mDFihXMmTOHpUuX5gh/b7zxBvvvvz+jR49my5Yt3HZb3tloe/Towdy5c5k/f37OPs+UDnD77bczb948ZsyYwYIFC1i+fDn33rvna9GqVSteeOEFjjzySLZs2ZIjKHvpDRw4kM2bN3P00UeTkpLC66+/zoYNGxgzZgzPP/88H374YYH3ZcSIEfzvf/9j9erV7Ny5k8ceeyzn2I8//kizZs2oXbs23377LQAbNmxgy5YtHHnkkXz44YcMGTKEUaNGsWbNGo455hguuOACQHw9v/32W+bNm8eGDRt4++23qVWrFldccQW9e/fmtttuY8uWLTnP109B//V48803GTRoEGvXrqVDhw707t27wOsDSElJYezYsTRs2JAtW7awZcsWGjZsuMd5v/76Ky1btiw0LT8rV65k48aNLF++nFdeeYVrr72W9evXA4U/2+zsbC699FIWL17MkiVLSE5O5rrrrsuT9htvvMGwYcPYvHkzjRs3znNs7ty5PPvss0ydOpXNmzczbtw4mjRpAsDjjz/OyJEj+fTTT9m0aROvvvoqVapUAeDwww9nxowZrFu3jl69enHuueeyffv2Pa5r+fLle/0+WrVqxS+//BL1vVIURVGKn8I0qn5nuPRCFqWYuOGGG0hPTyc5OZnhw4fTtWtXunbtSkJCAieddBKHHXYYn3766R7/a9GiBSeddBKVKlWiTp06/Oc//+Gbb6JThlepUoXTTz+dkSNHAjB//nx+//13evTogbWWl156iSeeeIKaNWuSmprKgAEDeOutt4p0XaeffjqdO3cmISGBypUrk5GRQdu2bUlISKBdu3ZccMEFhZb30ksv5cADDyQ5OZmePXsyY8aMnGNjxowp1Oz/4osvcscdd9CqVSsqVKjAgAEDmDFjBosXLyYpKYnNmzfz+++/Y62lVatWNGjQIKpr2tt/TzvtNI499lgqVarEAw88wA8//FDkjkt+bNiwgdTU1KjPT0pK4q677iIpKYmuXbtStWpV5s6du9dnW6tWLc4++2yqVKlCamoqAwcO3OMZ9enTh9atW1OhQgWSkpLyHEtMTGTHjh389ttvZGVl0aRJE5o3bw7Ayy+/zP3330/Lli0xxtC+ffscIf/CCy+kVq1aVKhQgZtvvpkdO3Ywd+7cPa4rmu8jNTU1T4dJURRFKX0U6Ohorb3at35p8RSn9OE3FyYlJeXZrlKlSp7t6tWr59muXbt2nu369evn2U5PL5qc7z9/8eLFvPvuu3m0eVlZWRx//PF7/G/16tXccMMNTJw4kc2bN5OdnU2NGjWizrdXr17cfPPN3HXXXYwYMYIzzjiDKlWqsHr1arZt28ahhx6ac661tsgj8iPvw5QpU+jfvz+zZs1i586d7Nixg3PPPbfA/9evXz9nvUqVKmzZsiVn+9NPP2XYsGEF/nfx4sX069ePm2++Oc81LF++nBNOOIHrrruOa6+9liVLlnDmmWfy2GOPUa1atb1e097+67/mqlWrUrNmTVasWFHkdyKSGjVqsHnz5qjP94Q+D+/+rVmzptBnu23bNm666SY+++yzHA3s5s2b8/gYF3YtLVq04Mknn2Tw4MHMnj2bk08+mccff5yGDRuydOnSHKE1kqFDh/Lyyy+zYsUKjDFs2rQpj6uHRzTfx+bNm3NcUBRFUZTSSdSjG4wxBxhj7jLGvOh+D4hnweKBMaa7MWZYWQ1tZIzJWU9PT+eiiy5iw4YNOcvWrVvzHe19xx13YIxh5syZbNq0ieHDh2OtzTfd/PD8PmfMmMHIkSNzzP61a9cmOTmZ2bNn55Rh48aNeQTFgspf2P5evXrRo0cPli5dysaNG7nqqqvylDdaVq5cyV9//cUhhxxSYP7p6em8+OKLee7jP//8w1FHHQWIFnvatGnMnj2befPm8eijjxZ6LX4K+i/kdfvYsmUL69aty9eU7yeaPNu1a8e8efP2et7e2NuzHTp0KHPnzmXKlCls2rQpx62iKO9Vr169mDRpEosXL8YYw+233w7IM1m4cOEe50+cOJGHH36Yd955h/Xr17NhwwaqV6+e77sRzfcxZ84c2rdvX/SboyiKohQbUQmqbrT8NOAgYB3QEvjJGNMjjmULnbI8mCqSCy+8kNGjRzNu3Dh2797N9u3bmTBhQp4BLR6bN2/OGcC0fPnyPAITQL169fId7OJRoUIFzjnnHG699VbWrVvHSSedBEBCQgJ9+/blpptuYvXq1YD4Bo4bNy7fdOrVq8eyZcvYuXNnode2efNmatasSeXKlfnxxx8ZMWJEoecXxKeffsopp5ySIzDVqVOHhISEPNd61VVX8eCDDzJ79mxA/IzfffddQEbQT5kyhaysLFJSUqhcuXKOtnBv96yw/3plmzRpEjt37mTQoEEcccQRe9Wm1qtXj7///puNGwuen7tr165Ru3UUxt6e7ebNm0lOTiYtLY1169Zxzz33FCn9uXPn8tVXX7Fjxw4qV65McnJyzv25/PLLGTRoEPPnz8day8yZM/n777/ZvHkzFSpUoE6dOuzatYt7772XTZs25Zt+NN/HN998w6mnnhrk9iiKoijFRLQa1SHA6dbaXtbaO6y1vYHT3X6lBEhPT+ejjz5iyJAh1KlTh/T0dB599FGys/ecFu/uu+/m559/pnr16px22mmcddZZeY7fcccd3H///aSlpeUZiOSnV69efPHFF5x77rl5TMUPP/wwLVq0oFOnTlSrVo0TTzwxX59BEHN469atqV+/PrVr1y7w2p577jnuuusuUlNTuffee+nZs2c0t2QPIsNSValSJWckelpaGpMnT+bMM8/k9ttv5/zzz6datWq0adOGsWPHArBp0yb69u1LjRo1aNy4MbVq1eKWW24B4LLLLuO3334jLS0tT3B9j8L+C3I/77nnHmrWrMm0adN4880393o9Bx10EBdccAHNmjUjLS0t31H/hxxyCNWrV2fKlClFvV17UNizvfHGG/nnn3+oXbs2nTp14pRTTilS2jt27KB///7Url2b+vXrs3r1aoYMkerkP//5Dz179qRLly5Uq1aNyy67jH/++YeTTz6ZU089lQMPPJDGjRtTuXLlAoX7vX0fU6dOJSUlhY4dO8ZwhxRFUZR4Y6IxqRpj1gN1rLW7fPsqAGuttWnxK158SElJsVu3bt1j/5w5c2jVqlUJlEgJm127dlG/fn0WLlxI9erVS7o4eejTpw/77bcf999/f1zSHz9+PM8991yhkRLKO2effTaXXXZZoQPttD5QSh2DQ67LBudjnSmLeRRXPvnk0fa1tqFm8eslv+aTbzE8kzhijNlmrU0J+v9oo8bPAG5GplL1+I/bryiljnXr1nHfffeVOiG1OOjSpQtdunQp6WKUat5///2SLoKiKIoSBdEKqlcDo40x/YClwP7AFqBM+agq5Ye6dety9dVX7/1ERVGUMCjjWi9FKa1EJahaa383xrQCOgENgRXAFGttVjwLpyj7It4EEoqiFBMqREZNRuZW+nRIok+HimTttpz0xjYuPySJC9tVZFuWpeub27j6sIqc1yaJjdstp7+1jRuOqMhZrZJYuy2bc975h5uPrEj3lkms3JJN/b1nqSiFUpQJ423EsueonVKOi17QvVKlSiVdFEVRFEVRFGUvRCWoGmPaAR8ClYDlyKxV240xZ1pry8wchNba0cDolJSUvoWcE1W8SkVR9l3yi56hKOWBCX1yx7wkJZo821WS8m5Xr5x3u3aVhDzb9atGHapdUQok2rfoVeC/wH7W2o5AI+BZt3+foXLlyvz999+BgssrilL2sdayc+dOli9fTkpK4EGqiqIoSkhEa/o/EHjSOgnOWmuNMU8Bg+NVsJJgv/32Y9myZaxZs6aki6IoSglRoUIFqlevXmisXyUk1HdUUZS9EK2g+ikywv8D377uwJjQS1SCJCUl0bRp05IuhqIoSsmjQqSiKKWAaAXVROAtY8w0JDxVOnAo8JEx5nXvJGvtxeEXUVEURVEURSmPROujOguZLnUc8Jv7HQLMBhb6FkVRFEUpd2RkbiVzxk4AsnZbMjK3MnymbG/Lku23Z0lEx43bZXvUHNleuy2bjMytjJ4r2yu36GA+RfGINo7qPfEuiKIoiqIoiqL4KUocVUVRFKU0oP6jpQ4N66Qo8UEFVUVRlDBRITJqdBYkRVH2hnbbFEVRyhjqD6koSnlBNaqKoihKiaDmckVR9kbUgqoxph7QEagN5Mwxaq3dp2anUhRFKe2ogKcoSnkhKkHVGHMGMByYD7RGwlK1ASZRhqZRNcZ0B7pXqlSppIuiKIqiKIqi7IVou9L3A5daa/8FbHW/VwDT4layOGCtHW2tvSIxMbGki6IoiqIoiqLshWgF1f2tte9G7HsNKFMzURljuhtjhu3evbuki6IoiqIoiqLshWh9VFcbY+pZa1cBfxpjjgTWIlOrlhmstaOB0SkpKX1LuiyKohQzYYeNgn06dJSiKEppIFqN6kvA0W79CeBr4Bfg+XgUKl6oRlVRFEVRFKXsEO0Uqg/71l83xkwAUqy1c+JVsHigGlVFURRFUZSyQ1QaVWPMR/5ta+0Sa+0cY8yo+BQrPqhGVVGUeKPB+BVFUcIjWtP/8QXszwipHMWCjvpXFEVRFEUpOxRq+jfG3OtWK/rWPZoBi+NSKkVRlJAJe17589/7hwmD98xHg/EriqKEx958VNPdb4JvHcACS4HBcShT3NCA/4qiKIqiKGWHQgVVa+2lAMaY7621LxVPkeKHDqZSlNJJ29fahprer5f8use+sDWd/m1FURQlPkQ76v8lAGNMKlAbML5ji+JTNEVRFEVRFKU8E5WgaoxpBYwA2iNmf+N+oYwF/VcURVEURVHKBtF66j+PBPmvCWwCagAvApfEqVxxQcNTKYqiKIqilB2inUK1PXCStTbLGGOstRuNMbcCs4Dh8SteuKiPqqIEIOypR3XaUUVRFCVKotWobgeS3PpaY8z+7r+14lIqRVEURVEUpdwTraA6Eejp1t8DxgLfAF/Fo1CKoiiKoiiKEu2o/56+zQHAbKAq8Ho8CqUoiqIoiqIo0fqo5mCtzQbeiENZ4o4G/FcURVEURSk7FCioGmPeIDcEVYFYay8OtURxRAdTKYqiKIqilB0K06gu8K3XRkJRjQYWA/sD3YHX4lc0RVH2io7IVxRFUfZhChRUrbX3eOvGmHHAadbaib59RwOD4ls8RVEURVEUpbwS7aj/TsDkiH1TgCPDLY6iKIqiKIqiCNEKqtOBIcaYZAD3+wAwI07lUhRFURRFUco50QqqfYDOwEZjzCpgI3A0UGYGUoFOoaooQcjI3ErmjJ0AZO22ZGRuZfhM2d6WJdtvz8oCYON22R41R7bXbssmI3Mro+fK9sot2SVwBYqiKEpZJdo4qn8CRxlj0oGGwF/W2iXxLFg80FH/iqIoiqIoZYcixVG11i4FlsapLIqilEIm9EnJWU9KNHm2qyTl3a5eOe927SoJebbrV43WiKMoiqIoAQL+K4oSBWGHjQINHaUoiqKUO1S9oSiKoiiKopRK9iqoGmMSjDEnGGMqFkeBFEVRFEVRFAWiEFSttdnAR9bancVQHkVRFEVRFEUBojf9f2uM6RTXkiiKoiiKoiiKj2gHUy0GxhpjPkJG/VvvgLX2rngUTFEURVEURSnfRKtRTQY+RATU/YB036IoSgRhB8nPyNxaAlehKIqiKCVLtAH/L413QRRFURRFURTFT9RxVI0xrYBzgHrW2uuMMS2BStbamXErnaKUUcIOku/fVhRFUZTyQlSmf2PMucC3QCPgYrc7FXg8TuVSFEVRFEVRyjnR+qjeC5xkrb0K2O32/QK0j0up4oQxprsxZtju3bv3frKiKIqiKIpSokQrqNZFBFPIHfFvfetlAmvtaGvtFYmJiSVdFEVRFEVRFGUvRCuoTgMuith3PvBjuMWJL6pRVRRFURRFKTtEO5jqBmC8MeYyIMUYMw44EOgSt5LFAWvtaGB0SkpK35Iui6IoiqIoilI40Yan+t0YcxDQDfgECfr/ibV2SzwLpyiKoiiKopRfog5PZa3dZoz5DvgDWFEWhVRjTHege6VKlUq6KIqiKIqiKMpeiEpQNcbsD7wJdALWAzWMMVOA3tbaxXEsX6io6V8BaPta21DT+/WSX0NNT1EURVEUIdrBVK8hA6rSrLV1gRrAVLe/zKCDqRRFURRFUcoO0QqqhwK3Wmu3Ajiz/+1uf5lBw1MpiqIoiqKUHaIVVCcDHSP2HQb8EG5xFEVRFEVRFEWIdjDVQuBTY8wYZMR/OtAVGGGMudc7yVp7V/hFDA8dTKUoiqIoilJ2iFajWhkYBexAZqnaAXwAJCNCazqwXzwKGCZq+lcURVEUpSyRkbmVzBk7AcjabcnI3MrwmbK9LUu2356VBcDG7bI9ao5sr92WTUbmVkbPle2VW7JL4ApiI9o4qpfGuyCKoiiKoiiK4idajaqiKIqiKEqZYV/RRE7ok0KfDhUBSEo0TOiTwoXtZLtKkmyf1yYJgOqVZfusVrJdu0oCE/qk0L2lbNevWvbEvqgD/u8LqI+qoiiKoihK2aFcCaoa8F9RFEVRygcT+qTkrHuaSA9PE+nhaSI9PE2kR1nURO4r6J1XFEVRFEVRSiVRC6rGmJOMMa8YY0a77cOMMSfEr2iKoiiKoihKeSYqQdUYcz3wPDAfONbt/ge4P07ligs6haqiKIqiKErZIVqN6o3AidbahwBv6NvvQMt4FCpeaBxVRVEURVGUskO0gmoqMiMVgHW/ScDO0EukKIqiKIqiKEQvqH4L9I/YdwPwdbjFUZT4s+jBRayfuB4Au8uy6MFFbPh+AwDZO7JZ9OAiNk7ZCMDubbtl+yfZ3rV5F4seXMSm6ZsAyNqQVfwXoCiKoijlhGjDU10PjDbG9AVSjTFzgU1A97iVTCmfDK4ecnobw01PURRFUZRiI9opVP8yxhwOdAT2R9wAfrTWlr1JY5VyT7M7muWsmwomz3ZCpYQ824lVEvNsV0itkGc7KS0pzqVVFEVRlPJL1AH/rbUWmOIWRVEURVEURYkr0Yanam+M+coYs84Ys9MtWcaYMjWYSsNTKYqiKIqilB2iHUw1EvgOiaHayi0Hud8yg4anUhRFURRFKTtEK6jWB+6y1s6y1i70L/EsnKIoiqIoSmlFo8jEn2gF1deAXvEsiKIoiqIoiqL4iXYw1UPAD8aYAcAq/wFr7Qmhl0opnWjoKEVRFEXJQaPIxJ9oBdX3gD+AD4B/4lccRVEURVEURRGiFVQ7ALWstWVqlL+iKIqiKIpSdonWR3UicHA8C6IoiqIoiqIofqLVqP4BjDfGfMCePqp37e3PxpjrgD5AW2CktbZP0YqpKIqiKIqilDeiFVSrAGOAikB6gHxWAPcDJwPJAf6vKIqiKIqilDOiElSttZfGkom1dhSAMeYwYL/CzjXG9AH6Aj8ClwLrgAuBA4H7gErArdba19z5XYHHEAF6E/CEtfaxWMpbJtER+YqiKIqi7GMUKKgaY5pYa/90680KOs9auygO5ToCeBmoBdwDvAWMBloAxwHvG2Pet9ZuAV4BelprJxpjagBN80vQGHMFcAVAxYoV41DkAghbgAQVIhVFURRFKRcUplH9FUh16wsAC5iIcywQj/lI/7DW/g/AGPM2MBC411q7A/GV3YkIrTOALOBgY8wv1tr1wPr8ErTWDgOGAaSkpNg4lFlRFEVRFEUJkQJH/VtrU33rCdbaRPfrX+IhpELeAVv/uDJE7qvq1s8GugKLjTHfGGOOLChRY0x3Y8yw3bt3h11eRVEURVEUJWSiCk9ljHm6gP1PhlqaAFhrp1prTwfqAh8C7xRy7mhr7RWJifGSrxVFURRFUZSwiDaOap8C9l8UzZ+NMRWMMZURN4FEY0xlY0y0EQcKS7eiMaa3Maa6tTYLGUxVoLpUNaqKoiiKoihlh0KFRWPMv73zfOsezYC1UeZzJ3C3b/tCZJDU4Cj/XxgXAc8aYxKBuS7tfLHWjgZGp6Sk9A0hX0VRFEVRFCWO7E2r6WlMK5JXe2oRP9JLosnEWjuYKIVSa20mkOnbXkDEIC5rrT/E1SnRpAuiUQW6V6pUKdq/KIqiKIqiKCVEoYKqtfZ4AGPM/dbaO4unSPFDNaqKoiiKoihlh2gD/t8JYIypS+5oe+9YPOKoKoqiKIqiKOWcaEf9n2yMWQ6sRGKqesv8OJYtdHQwlbKvkZG5lcwZOwHI2m3JyNzK8JmyvS1Ltt+elQXAxu2yPWqObK/dlk1G5lZGz5XtlVuyS+AKFEVRFKVgoh31/xwyfWlKMcVRjQsankpRFEVRFKXsEG2IqBrAi9ZandFJUUoRE/qk5KwnJZo821WS8m5Xr5x3u3aVhDzb9atG229VFEVRlOIh2pbpFeDSeBakOFDTv6IoiqIoStkhWkG1E/C8MWaeMeZb/xLPwoWNmv5LP+pzqShK2Gi9oihll2hN/y+7RVEURVEURVGKhWjDU70W74IUBxrwv/SjPpeKooSN1iuKUnaJSlDNZ/rUHKy1r4ZXnPiiAf8VRVEURVHKDtGa/i+K2K4PNAe+A8qMoKooiqIoiqKUHaI1/R8fuc9pWVuFXiJFURRFURRFIfpR//mRCVwWUjkURVEURVEUJQ/R+qhGCrRVgAuBDWEXKJ7oYCpFURRFUZSyQ7Qa1V1Alm/ZCAwAro5TueKCxlFVFEVRlJIl7Li2GZlbS+AqlOIi2sFUTSO2t1pr14ZdGEVRFEVRFEXx2KugaoxJBL4CDrbW7oh/kRRFURRF2VcJO66tf1vZ99ir6d9auxvYDVSOf3EURVEURVEURYjW9P8k8I4xZgiwDLDeAWvtojiUS1EURVEURSnnRCuoPut+T4rYbwEdmaQoiqIoiqKETrQB//eJyY01PJWiKIqiKErZYZ8QQKNFw1MpiqIoiqKUHcqVoKooiqIoiqKUHVRQVRRFURRFUUolKqgqiqIoiqIopRIVVBVFURRFUZRSSUyCqjFmTFgFURRFURRFURQ/sWpUJ4VSCkVRFEVRFEWJICZB1Vr7YFgFURRFURRFURQ/UQX8N8Y0K+DQDuAva212eEVSFEVRFEVRlOinUF2ATJcKYHzrANnGmI+Ba6y1q8IsnKIoiqIoilJ+idb03xd4EzgQqAy0BIYD1wBtEYH3v/EooKIoiqIoilI+iVajeg/Qwlq73W0vMMZcDcyz1r5ojOkDzI9HARVFURRFUZTySbQa1QSgScS+/YFEt76F6IXeEsMY090YM2z37t0lXZQySUbmVjJn7AQga7clI3Mrw2fK9rYs2X57VhYAG7fL9qg5sr12WzYZmVsZPVe2V25Rt2ZFURRFUQonWuHySeArY8z/gKXAfsClbj/AacAPYRcubKy1o4HRKSkpfUu6LIqiKIqiKErhRCWoWmsfMcbMBM4FDgH+Ai6z1n7mjn8IfBinMoaGMaY70L1SpUolXZQyyYQ+KTnrSYkmz3aVpLzb1Svn3a5dJSHPdv2qOimaoiiKoiiFE214qtpOKP0szuWJK6pRVRRFURRFKTtEq9ZaYoz51BjT2xhTJa4liiPqo6ooiqIoilJ2iFZQ3R/4BLgaWGWMGemEvlI/gMqPtXa0tfaKxMTEvZ+sKIqiKIqilChRCarW2rXW2uestUcDrYFfgAcQX9Uyg2pUFUVRFEVRyg5BRrTUc0ttYEOopYkzqlFVFEVRFEUpO0QlqBpjDjbG3GeMWUju6P4zrLUHxK1kiqIoiqIoSrkmWh/T74D3gSuAr6y1FsAYk2CtLTOR2zU8laIoiqIoStkhWkG1nrV2p7dhjGkLXAL0AhrGo2DxQMNTKYqiKIqilB2iHUy10xhTxxjTzxjzMzADOAzoF8/CKYqiKIqiKOWXQjWqxpgkoAfQBzgZWACMBBoDPa21q+NdQEVRFEVRFKV8sjfT/yogG8gE7rbW/gxgjLkmzuWKC+qjqiiKoiiKUnbYm+l/JpAGHAEcboypEfcSxRENT6UoiqIoilJ2KFRQtdZmAM2B8cAtwEpjzGggBUiKe+kURVEURVGUcsteB1NZaxdba+9zMVP/D5mNKhv4xRjzSLwLqCiKoiiKopRPijQzlbV2krX2CqA+cD3QNi6l2ofIyNxK5gyJ7JW125KRuZXhM2V7W5Zsvz0rC4CN22V71BzZXrstm4zMrYyeK9srt8i2oiiKoihKeSDaOKp5sNZuR0b/jwy3OPFFB1MpiqIoiqKUHQIJqmWVkgj4P6FPSs56UqLJs10lKe929cp5t2tXScizXb9q3m1FURRFUZR9mSKZ/hVFURRFURSluFBBVVEURVEURSmVqKCqKIqiKIqilEpUUFUURVEURVFKJeVqMJWO+lcURVEURSk7lCuNqk6hqiiKoiiKUnYoV4KqoiiKoiiKUnZQQVVRFEVRFEUplaigqiiKoiiKopRKVFBVFEVRFEVRSiUqqCqKoiiKoiilEhVU9xEyMreSOWMnAFm7LRmZWxk+U7a3Zcn227OyANi4XbZHzZHttduyycjcyui5sr1yS3YJXIGiKIqiKEpeVFBVFEVRFEVRSiXlKuD/vsyEPik560mJJs92laS829Ur592uXSUhz3b9qtp/URRFURSl5FGJRFEURVEURSmVqKCqKIqiKIqilEpUUFUURVEURVFKJeXKR9UY0x3oXqlSpZIuiqIoiqIoirIXypVG1Vo72lp7RWJiYkkXRVEURVEURdkLqlFVFEVRFEVRSiWqUVUURVEURVFKJeVKUFUURVGiQ2e7UxSlNKCmf0VRFEVRFKVUUq4EVWvtaGB0SkpK35Iui6IoSmlGZ7tTFKU0UK4EVdWoKoqiKIqilB3KVTdXB1MpiqIoiqKUHcqVoKooiqIoiqKUHdT0ryiKoiiKopRKypVGVU3/iqIoiqIoZYdyJagqiqIoiqIoZQcVVBVFURRFUZRSifqoKoqiKIqiKKWScqVRVR9VRVEURVGUskO5ElQVRVEURVGUsoMKqoqiKIqiKEqpRH1UFUVRFEVRlFJJudKoqo+qoiiKoihK2aFcCaqKoiiKoihK2UEFVUVRFEVRFKVUooKqoiiKoiiKUipRQVVRFEVRFEUplaigqiiKoiiKopRKNDyVoiiKoiiKUiopVxpVDU+lKIqiKIpSdihXgqqiKIqiKIpSdlBBVVGUEmfRg4tYP3E9AHaXZdGDi9jw/QYAsndks+jBRWycshGA3dt2y/ZPsr1r8y4WPbiITdM3AZC1Iav4L0BRFEWJCyqoKoqiKIqiKKWScjWYSlGU0kmzO5rlrJsKJs92QqWEPNuJVRLzbFdIrZBnOyktKc6lVRRFUYoL1agqiqIoiqIopRIVVBVFURRFUZRSSYkIqsaY64wxPxljdhhjMkuiDIqiKIqiKErppqR8VFcA9wMnA8klVAZFURRFURSlFFMiGlVr7Shr7YfA33s71xgz2Bgz3LfdxBhjjTEV3HYfY8wiY8xmY8wfxpje8Su5oiiKoiiKUlwYa23JZW7M/cB+1to+hZwzGGhhrb3QbTcB/gCSgErAX8Dh1tq5xpgGQE1r7ex80rkCuMJtHgL8E96VKIqiKIqiKPmQbK0NrBjdF8JTZQNtjDFLrLV/IYLrHlhrhwHDirVkiqIoiqIoSmDK9Kh/a+1W4DzgKuAvY8wYY8xBJVwsRVEURVEUJQTKiqBa2bee5j9grR1nrT0JaAD8DrxUjOVSFEVRFEVR4kRJhaeqYIypDCQCicaYyt7gqALIMMa0MMZUAq53+2oaY+oZY3oYY1KAHcAWYHd8S68oiqIoiqIUByWlUb0TGczUH7jQrd9ZyPm/ACOBpchAqh+At5Hy34yEu1oHHAdcE7dSK0qcMMaUFeuGUobw3itjTGJJl6UsYIwxJV0GZd9F6/lglOio/2iIHPWvxIYx5kTgeOBea+2Oki5PWBhjEqy12WUxfWNMFWvttmLIJ8lamxWPtEsKY0yleL3Hxhhji6mCNMZ0BqZ770EI6RlrrTXG7A8st9budvvj8n7571VxvGfGmHbW2plxTL8zMMVauytO6R9krf09Hmn78kiw1mYbY5KttXGJclMc30jEu1XPWrsqTvlUiMfzjrxHxphaQP38ohMp+aPSffnDADcCvxpjzirhsgTGpylqA+AqZBOWRsRLxxjT2kvfvz8sjDFVgenGmDvjnE+ytTbLGNPKGPNUWdYcGWNSjDFJbnOEMaZ9PPLxNY6nGmOS4/BMDjPGHGGMaQq8Sl5f/JjwNYx3A1nGmGvd/lC/k8j8jDE3I5at0DHGtDfGHOM2vzPGdAgpXe9b72yMaWmM+RfwX8Q1LXSMMT2BD+KRth/3rBOB//jijsfrucflG3E0ch0ugDnGmOPDzsB1eHcZY5obYwaEmbbrMBpjzJVu1zAgbm2vMaam+80wxvSJVz7FiQqqpQhjzCXeSxYvrLWfW2tTgBHAW8aYCWFV+B7xNm+4Hmq281l+1xjzmTHmUOsII3+XzgnATGPMC8aYQ337w7y+BOAp4BRjzM9e58HLJ4yK34g/+I+uonwZ2OLST9rLX4uazyXGmP8LM80CuBR4zhjzLNDSWvuLyz+052KMOcEYc5ExpgbwMaIBCU1z5J5rd8T1aSzwk7V2XcTxMOgL/BsYZIyZb4w5IczvBMAYc54xZrgTIh9FBrXGg8OBIcaYmcB31toZLv+YrsPdi0qI8HA18BHwuaepj4Pw9SjSgSgOl4xWwJXAg5CnAxMz8f5GfJwJ/GKM+QJYYK392uUfyr1zz/4dY8xp+DqMpvBxM0XlEKCjMeZToAswxJd/mPVWU+A6Y8xRwChgSdh5lAjWWl1KwQKkIj64a4GriinPGkjvLht4EqgTQ1oJYZxTlLyAa4F3gTXAciATqBtWfsBFwGak4foKeASo5ztuQrqeisABwGBgPiK4dAjxOhKBY4GZiD/4//nTDuM6kKgbm4D9w7gnheRTATgSGIAMnhyBNJA5zySk6zkPGI1U9B+Ffb98z/1x9449CfQGGob9frm0qgJDge3Al2HmA9R3395G4Js4PXfvm+8JbAW+RyxDKZHnxJBHNeA1ZLzDg8DZQI0wnwcypuKPiPcplHqxkDzbufrryjDuky/d4vhGPPfE44FtwCLgbP/xEJ57HeBiYLJ7tw4J+1pcvXUg0k59jwiqx+V3rTHmcwTwPPAT0mGsDySFmUdJLKXeR7W8YYzpCzwG/AncZK39KqR0uwLNEWG4GtLIL0Eq5U7AcLf+b2vtxzHk8zLwHdJo/WWt/cEYk2at3RDbFeSk7/ldHYUIkB2RRrgh0A9oDTxqrX02hLxSEAGiGSIQHQvsD7xtrX0h1vRdHpH+S4cD5wLnAG8CD1trt4SU11/AGOSeLQJusdYucMcSrfNjDJj2R8jzvsptV0E0B6NjSbeQ/N4EWiIm1NZIxfyGtXaNOx7YF9N7JsaYixAT8DRgBvCMtXZRrOlH5DUHeAf5FjsC64FJwBfW2u0B0vO+j6rId74KSLPW/u2ON0bql27AQ9bae2Isf5IVl5LOiAbnd0Qj9bi19u1Y0i4gv++B6YhAeSNQC3g1rLyMMcuBJ5AoMm2BlcAEYEKsz9sYUx2pf5cjguMw67MGhPQ+5eszaoy5DBlofLO1dkJY+RTHN+LS+gDIAl4EHkYEyjustd+74zHVXy6NlcA3QHXkPX7EWrvCHYulPvG+yUuRQeM9gdOAg4B5SL210J0b2OfX90z2AxYAn7nf6YgF4s8g6ZYGVFAtpRhjngBuQBriW2J5yZw5YKHbXI70go9BGrGDkYbxAKA9cIy19ruA+VyACFe7XB5HIRqcKUjFPxqoCXxqrV0W9HpcXg8h2tN/u+0E4DDgfaQSWwHcaGMccGGMSUNi8y53aXcGTkS0YK9bawP5mhljzgZORXrafyP3Zxcys9p+QG3EbPsrMkXwzhiv4wykkTrGyFTDtyImtfeBAbGkb4w5FnjfWlvHt28Uojm6OZZyF5BfTeBDpMLfBXRFNAkpSOX8TkiN/gzgPeSdvgnRTI0Bnor1ebj0WyBarludefk4RHNUDxFcv7XWTgqQbiNEe74W0abPAP6FNFjpwM+I+0RF4CRr7ZchXMuPwHPW2kxjzOWI5nAecL+1dmqs6bs8agFXW2vvd9uNkW/xLGAD8Ky19ocY0j8U0dYNMMYkAycARyOxu+cDX1trp8eQ/juIBvBxZDrvg4DPEaF+gzsnZoHLpdMcqePHAJustTuMMRcCtyPv3PchCXcziOM34vKoAgwCHrDWbnGuBlchLi3fA7dba1fGmMcFQD9rbSdjzNHABUjb+AEifMckKLnv+xrgK2vtHCM+t4ci71c9pA0eYa3dFEs+Lq8XgO3W2huNMbcjz2Qx8C0w1eu0liVUUC1hnJ/NoYjAsgpohITfqoxUkJ8jmpEXgSE2wGha91FcDjRFBKKJ1trXfMdbIj39/ay182K8nseQD2MY0iB2BE5HzER/Ig3KrbE2jsaYk4F7gAs9raDb/wjwG9Lgb7DW9itCml7PN9Vau9nXQ20G3IUIpl85X7zzgVRr7cUByz8F8bkbjQgMXyMNy5/I/VuNCKyjrLWPBskjIr+6wA5r7UbfdR2C9PAPRhrLQFMMG2N+BuZYa3u77X8B44BmnjbYyACFadbacSFcS5JLe67brgC0QSr9f+HC1lmfz2eAPGoCZ1prX3HbdRDLw/nIdzncWjsylutw6eYRFowxqYgmuhOi2bneFjGqgTGmO/AcIlzNRUz9fyF1SyLQBBGE61prn4j1Glyelf0aYFev3Y9YB75ChIn1IeSTJ8qD8y9sAZyB1DNdrbVrY0g/z8hv992cjHSCE4BBQaxDRgZlfgfUstbuNsYciLyvPRAXrNesta/GUO6jgLbW2hfd9hCkPmmJ1C3VkHrx/5A65twgbUlEnsXyjbi0K1trt/u/F6eAuQNx0ehgrV0aQ/ptgVXW2tWufklDns95iFveS9baD2O8jPzybYm4Mp0ITArDUmdkzMls7/m67V6IG8AcYLy1dlqs+RQnKqiWME6wugUxa3wN1EWEVIOY4pchlf0f1trmMeRTEfkYjkNMpYuB96xzTI8VrwIxMgr/CWCZtfZSd2wC8IG19iljTFNr7R8h5JeODAyqCAxEBLuKSA/7SKAx4gd0dlHyc5qINxFT8mTE93IEUmENQir4L5zWaqd1puYipO93XeiLaE6/Ab70a2uMMVVtSCb/KMp0HtID/6iI//ME3vsRDcQU4CHgFcQ94jF3XltEY9AkDGElvzK49VREoDjQa7BjTDuPuc8JX40QQfICpJEOpAFxadmI9P3X0hTpCEVlEcinrEciPtZVER/ut4Bf8tNyBTVr+p6/X3jwfAa97SaI+8zFYWiLCilLNcR/fH4R/xeVVtEJmrWttd8ELN9RQFVr7XhfHVARqYtPRrS3lRGNcZHDFhmJ6nApEmf8UWvtj25/C+Sb2IkI80mI8mAB0Mda+1eQ6/HlG7dvpAhlCC1UWcQ3mIhYIM4E5ltrP4kh3cj75M+nEuJmMs9au8l/LBby6QSfigzeHGCtXRxr+sWJCqoliPsQrkX8VdYjWs1HgWTEcfwwxIRdB/jTrzksQh6RH0hd4CREYK2PmAXfstb+FiDtgvyhOgCfIAOdFiIuDJ1i1HB5lXsVREDc5Sr6J5GK5E9EUP3Kiin1IGRQUjtr7eYi5HM3MiL3H0QjlIr02D9AzOX3WWsHx3AdNRDNphc39TJEA+G5S3yL9IZjNjWFUdkVIb+myGCzo5B71g7prOwyxnyLDLIZFDDt6kCWjTLGaKRWLEB+hd47I1EUqllrVxcxXe8dLjD9WJ+bkbjTz/k0Qz0RF5MqiIVjnA3JFO/yy1fIdQKrCSIAFwfGmCsQ39ZdbjtfgbU4viP3fh+OuLDcH6SedNrNfyEa2k5IJ/tp63wffeelIBrcJ4A346EldPkU+RvxdXxSkE7cNv/+eJTTpV9oRy3SWlAaKex781+fMaZifp3V0o4KqqUAIz5+/4f0rjcDI62140NItwYi8CZGNvLO9HQCIiSvtNb2jSGfzkjkgJnANlfZ9EDMyu2BM6y1Y2PQ3Hja2obIyOWjEB/F96y1E434XDZFtAkrEZPp18Bn1to7okjf37uthjQY/0ZM79ciGs/6btlg3WCBIBhj7kXuywBr7UO+PG9EOhALXNnH2BB8iYzEGJ0di+BWQLpeo1IR2OWrCDsjI6arIGG3qiLXmh5DXu8iptFCNRpBGzS/FjBaDVuAPPxax9cQV4tfQkzfIALIz4jZcoi19hF3rB4SUaCTO/1za+1LMeR1MmKxeM3mhnAqSGCN60QcQXCC4TREAXCHtfY5tz8uwrXXcXKduQrINN9LIr9J46wosdwzIwNpjkI6742Rzu8TnqDl6yw9gJi2M6L5ZgpRSoQuRBpjHkdcVUYjWsac2NIlKbAWMS2vzaqGdLLjNeFCOyRaz5devkB2Ac+q1H2LUWNLQeiB8rogPU5vPQ0xzTyDq1yQQTRB066JDGQai/i5vopo7i5AhIcq7rxWQKsA6Se638uRASx353POfxCT0xUh3a9vEHN/L2QK3W+B+xCzSbI7JxnRFo8IkP5x3jNBRvffg/jyfYD4f3nnxRrOp5dLdylwum9/K2Tg1o/4wmwFSL+L+70A8QuNx7tbGRf2hHzCwyARGH5HOjDnxJDPeUhHyr8v1HA+/ueJDC47Lg736xpktP2NiKY533cp1nfL99zXIJ0e//vVFhmhfWoMaScgloZPET/YE8Muv0vHq19qIBrhIxA3nMSw8kI6o6sRP94Mf95xuI5/IVOBr0YsTQPwhUEK+V1LRAbHXufq5s+AXhHnPIyMFShq2q2QCCsNwyhrPukbJEzfRHJdrvYLOY8ERElzofs9MB7X4vIajSiDkuOU/s2INTbTf5/838m+sJR4Acrrgvg7foiYaPxx2xoBlyCC5UR/BVrE9P+FDFxa4dIbg/gQTic3NuiviLYz6DVUQkYWd/LtyxMXEBllOhVoFCB9A1Rw6zWAB3ACttvXGxHEvwHOj/hvxWjzcL/9kJBaDyC+vP4G5jXEp/cVxM8sjOefjPjQbkP8apv7jh0cQ7p1EbeFuYh2+WjfswpNwHMV4x+I8HUf4m/bjLzxXysB3WPM5w/gNN/2MUQ0uiFcS39Ew3Y9sNH/LoeUfhIiqGYincfXwix/IfnegwyeHItMjBBWurWRTu+Lrh55AGjjOx5m/NevXf2xARFczgfSQ0y/GjIKPwsRvv3xZSuEmM8Pri5sgggX7wOvI8H4m4WURz3EP786orn1BuregwykOtJ3blT1o+/8U1w9+Dcy6PcdRBHRIE7vbhOkUzXZ/XZBBqLFmu5Brh5fgrSN85B2+Fqgekhl99qOAUh7m4C0ZS2QAX/Nw8jH5VEdyEAiL/yBCPkmsixlfSnxApTHBRlBPgPp0b2I9LTruY+ztjunHdIjDtxYInE/5yEjbnGV102IWWUQ4ovZNkC6nnD3IDIqHfIKp97x5ojj/vMh3LOpyMCy0yP2V0UEpUNjTP8/iAD/FvCCa1TaeNeGuAP8jC/YdBHTr4IIkYnIIBlv/37A/5CG8mHC0+TMQLSZo4jQSLh3K3APH9GmTnbpT0Zi8C5FhOPfXN7DEAEmcGOPaB93Ruz7C7g0jHvkS7Mt4uu8G4mXGVraEflcjcSvfQMYiWjWK7ljd1PETqPvO6uNaG26Ib6OZyBuKumIS8wCROvyCOFqC5siHZQ3ESHyRqBmiPfrKMRtBUSL9ygi8D3trjU1YLp7XD8yOn4M4iv+UEjl9wSWxogA0cB3rAPSUf0aiZcdNA+vI3+6S2szouC4E/HPB6kjY7HOtUSUGg8gMyydhQh73yCWvxpB03bpJ0Rci19r3sa9Wx8jAn5HfAHsA+T1PdJuHe62uyJWuinANSG+uxXdd3eQ274fsQDOQAa7hZKPd7+QDtdFSMzf2cAFYeZR0kuJF6C8LUgPaANuBh9EszYZMfluRcL6eKbbMMyA/dzHcYzbnomEdIo13QTgWWCg207K5/iD3rUESP829/F5ldhFSO90JTFq6QrIrwYiQExH3C8+QgSwy7wGhoDaVFdp/YmEBlmICMPzEK3Ed+4+rkEEvz4xXIO/J30Fohn+CGl8H3b7D0Y0IlWC5uPS8aIjXI/TziAxZre4d/kD4PJYrsXd+y8R02V3pJM1wR0PVVOAmAD/QMyyixG/Wu/Y/UGvhbwduAMQobIzoon+EGkkr0UEyUAmTpdONqKt/RBxHfnFvWOT3bPIRvyeY7lHJyNC/RmIENwWEYJ6uXs0HtFKtgvpmTRHBhf59x2OCPk/4ZuRrAhpesJjdcTVZxDQM+IaFyMdirA06gvd/R+Yz7Fu3nMnhvoe6cRf4dY/QqxDXxKCxhYRfB+O2GcQt691yABTYrlfiKLmVfddDHLfxZWufjnZvdNrkc5w44B5XEVux8dfV1ZEtM5bcO1kCM+8lvsWByOWv+VI3Xuyey4xaVWRzk8zxBWjGuKqloj4ofdDNMajY82ntCwlXoDytiCapu8i9u1CesQHIz3HCQQUJJAebwvfdgLS+13oPpzvCcmkhWhovo3YZ3yNwSNeJVbEdA8H7vWVv4ZbrwDci5i2JxCybxHSabgXGYiQjgjaoxFB8pygDQniX7cMERqGIWFkjkZcMnq5PC8B7grpOs7B9eTddlfEX3QbYuIu8jOJSN97vpcgPsiPuO3/IFEXwnoeFRDN052INiIbCd/jf7djadz9QmSyyy8J8V2cinQsbkM6kIHcMcjtaA1CGizPN7y2e89eQEzz/YqYrqdNTUTcCt5DTLMPIEJYmruWI9y7fBgyQ1XO8ytifue6+z8PEUgXIdrNJa5e2YgI29nENhWzp1Xrjgyc3AQcn895h8X4br2LaASHujK/E3G8bdB7lU9edRAhbDeiuY/ZhB2R/mVIeDsQy80qxAVgLKIUCewqg3RK/vK/z/jaD0SjPiuW79Clc497DtmIZv4ZpK78EulovYFYa16JIY/VwFmFXMs0YrDWkFsver77ZyNC/hs4P32kk7ooxnvVzN2n+e75fua+x0lIB+5npLOVTRz87UtiKfEClKcFMZmORBrdOxCNQSa+gT+IOW0yvjnli5C+QcwND7jtnA/HVZTZ5GpWw9DWHuY+ltsiy4v0kNfjE5qLmLY3OOoyxCTnn995P8QclI3E6YvlGupHbB+JaNYuc9unIObz/wRMvzrSYz8QaaRec79H4sy+Ib9jtRGh5SPE97KR71hXfP6eIeV3FqIV7ocI453972PANA906dV126mIxvM5Vxk/i8RkjbXs3vdxL9JQ1vMdq4+46Iz23oUY0u+ICFwtfdfTHhEqYp2n/EJEWD0SEYbfRywBZ4T8nNshDXk2ogFu7Mrf0i1t3XXG3HlEXGSyEVeYD5CO/EvEMMDQ/z4iPn1LfPunIRaIBMQXMhRtPREdKaRz+jsiSF5PeAqDs5DZpkBcyd526z0R/9sU//UXMe1vEC35Afh8ON07Z4DjgS8IMAYhIp/6iIZ2olv8Ps9p7rcaRfSt9aXRA9H+3oi4lCT7jnmC5QsEGGCWT14v4SxjSAfYc+9piLTP13j3MGD6xyHm/R/ce3Se+2ZORDSsFyNuf6eH8X6VhqXEC1AeF0QT9TkyY8sW8jq5Pw18EjDdJxFn98mRlRPS+M9yFU+sFX4Kua4LdyKap6FID7IhYs4aTwDfVHyDsdz6/7nr+hARUPyCUCcCNPS+e3Im4goxAfF/utJ94Cciwld7d14VoHLAe3U/0jlJcRX7BYiw+gFiFjoySLp7yfMwJObrO4ibQe+gFXxEut0Qga6t7x5WcxVzNjKpQxjlPxdpVD5FNGtep6U+0vgORzR7XWPIw3vHvJBwTd12PcS6Ecj/sYC8vsL5ISIdnzFII/OW950GTPcMYKZvOxnpjDyOdFSeBo4NofwpvvUrkDrrRwK69RSSj/dOdcOn9UcGNP7k3om7g3zzEfn0QeZXB+kQzXLrdRDtVJMY0z8Z6TBWJh9fcHcPs5FJEMK6dw3d73NeuojAd5VbD3TPEOvMGPct9kN8Rv2WiB6If2csZv+GiOCbiFgVn0Q6vW8T0MxfQD693fP9BLFCtCKvP+w84JQY0vfqlKsQF7VBXj1CriAeykBK9349jPglP4d0sNLCulelbSnxApSnhby961RksNQUpBd8GmIW2EQAXzXEHLAJaXh/Qua5BzFneg1AS0QoK7IPGXl9RT9GTL5vI9raC1zDNQ0RlGciAlrUPUZfGf2V4DAkCkI9l+/LLu+78GluCK65ew4Z+fm9q8CuQbTd3yImlatirIANInRNQoJve/vrID3htxFh5cai3KtCno2/0k1ChPwnEJPZSGIbUNEa6ZD0y+/9RHzLviOgBj2f9Kojo33/RAabHR7xHl9DCKN0Ee2zZ4Ho4r6PX917EfO1IB2U1xAtZAvERNcPEcBH4zpDAdNuijSukYPl6iNalZfcuxxLHqmIxqxfxP7nEFP2B4Q7ivlgd01v5HOsD/BjCHl0cPelJeI7mOH2P05AJYEvbc+E7Y03+ADpYJ/pnnmgDm9EHp6m3kR+A8gsh9mI2X9hSM+kEtKR/xaxZPXGRV5A6vrrA6Tp1Vm9kXjLh/qOVUG0zx+47z+mwW3kNe9XRUbjT0SiLvRy+wcAUwKmn9/gvONdvXWJb199cgXXoNrUCuSt549Axgp8i7S5RxNSZJrStJR4AcrL4irFQUjD+B/gALf/AHJ9cdbi/P0CpP85ub6C1+EbOEGu31e3yAYnyrQ9AaGGK+N1iBD0O04Ac5XZMe56Ase9Q6bgfBPx49wUcewgZJarT2OtvFx6zREB4mlES3eNqwhqIc7p1UN69hnIaPWvyBuG6iBXScesWUEardsQLUFF3/7GiAD5GgEGn/jS+R6Z5zxyv/duNUP8ou6M8Tr+i29AA6JN+xQRXgbjtF2EN9DlTkRL1xkZqHETEoN4OCFpDBHN50pE4PNrCpcTwPfV9z2mIB3Ent6zIG9nuD0xjv5178/9iODzEdDNd6ypq3eygb4h3avWSB35DyI4xhx/MuKeJCKC0HCkYz8Z0U6d7p5R81jeL/cezXDf9S1IRJJxiH/kZkSA/Z5wXCReQFw9ppNXIDodqaO9EedFcjHwvV+JEfubIr6jkxBf6BeB32J4fyu7e34BebX2frP8+e7bD3UUO9Iev4BYGF9EBiJ2iOX9QjqHn7j739w9+8mEqDkvpAznu7y/JaS45aVpKfEClIcF0cyNQUbivoJoHefi83tETILPBkz/dGC9b3t/93Ef7dtXExn40DRA+l7v9x185nxk0MmHRJhJCa7h9AIxf+gqjjfySxPpRe7nL1sR86lGXoHxaERL+xHi+N4tSPkj8vBcLzxBLh3xdX2FACHB9pKXQQTUpYhJ+XLEnOb5Rj0D9I4h/SPc++r5ckUGqW+KNNBNiW0gTXNE8zsJ0QT7A1ifS+4Ap/NCvHfHID6DHwP9fftXEtAtI+Jd9Z5/LcQy4DVqb1FEVwnff2v79r2GzDzkfxcauWf2GLkanFgGndVA3GEeRzo9r+DT0iKawsCxf/O5ZwmIG9F3SD0WkxDsu28XkXcChBuQQaafIIKrp12LxbqRhPg8/woc4ds/HRiIzD53SQjXcglSn5+OWGRWI9rNwFYTXx51kTYkhXw664iP5EhE8C6ya4nvGp7F+dN69x0ZwPqlqwP+5fYHjtWKhIQ7zC213D5/R/449/xjChnl3tn3kE7bX4jy43ZyIz7cH+M3WBURqu9DFCq9kTbd344lIYqXwBN6lNalxAuwry+Iun8NMruKfyaqu90L/FwIeTyA63HiTEuIWXkkuULm2wSYrcmXR2PEN+1Or/JyFc2rbj3MYPJ93D37zlW+/oFUg4hxJKOrQLLxTSCAaFm6If5RHyBmm6AjvVOQgWQvIoPmLnL7j0S0Ky8SQyzAveR9m6scP3CV9D2IG0MscVNbIybxfEcrI6bUUcTo++zSaoJoJl53z/9K8rqDDAFOjiF973sw5D+gogYy8PCzGNOvgQgQkxEz+bmIBqkKMvjlRwLGnwSeR1wTjkcE7S+QBvd+ZMTvPPfdhBG/2H/vKyMm7Gfc+3AbMVodyCugNnDX0d5tJ7t7OBdxKyryO0yumTwN6cRtRoRSL0ZyKlK3mfzKFMN1PYW4eRyMCBVziTDJEptb0S3Aub7tuu657EZcSgJHxEAExWykM/UpUhf2dkua77zAYa/cu/QhruPgnsNIZHDufW79x8h7VsQ8znLXsRGpD5chLl6PuPvXilytc1ixha9G2pXLkM5dX2QA1RWx5INoTLORCTzeJDc28ljEjWEA0jY2CeM6SttS4gXY1xf3Mfq1kEm+9VNdZRZoKj1ye6b1/dtu/QhyY8b9y73UgWNnItqy+xEfpdeREeXryG2YYxK8yNsgJriKrBYiaM1HTIzex9o4hOfSBRHoVuOb1QppLPu4yqDI2meXRgtEIzDVlfk9ZJTmvxHzdbarhMPy56xH3kFmtZCG8kv3vGKayQkx628kIpYoufFTj0UEiTBnJDoG6RhNdI3LGSGk6b2rqYjAOxdphJ9CIkl4EztMJKAWx/dNfoAI7ye6d/c3RGOUhPjfBn6H3fs1CIlO8Qe5QsXbiJbwYPJOKhHE6tDD3aMH3Hd/JeJb2w0ZOPkSuUJRLJM6eILkLe6+/4gIFONwI8mRwXuxRvcYjmjrHkU6D4vddTUin0DzMeTjdXgaIhaozxDN5xmx5uG7Vw2QztweoZqQ8ITeqPIifY++d/d5ZAzCKMQV5iukM7QO6fROcu90TAM0EavJh+59fQYRvvzP/DsCmuNdGmmIlexHxMrU3X3rH7tvZqV7D4JO4uLdr/3JjRLTCGmzbiK3ftxjMpyA+d3qyt4NaR87IG3xdqQT9jkxTnxTWpcSL8C+vCCNbbavgskz6AXRts4hhtHLheSdjPhC9XQfav8Y0vLKXQkRSh5FNDc/E9LUjL487kG0KF7s1ETE1+51pCHr67+HAfJJiqg4nnbPaAp5hb2YpgZ0DcbPSGOVimiKHka0UD+5PDvEkL73Dp2MCMQrXMXrjzMaWrxG91y+RwR8vz9ZAiKEB57VBQnfFfmNjEDM2gcifss/IIJY4MEo5DW7v4V04B5EXCaSyQ2DE9PsSogG+k/f9tfkzg53KS5iRox5VESmxxyKWB9eY0+NXSyN4hJyBeDx7juZSK5rxveIoB84nI/veaQjAlBbpEPcwuUzmXAGtJ0IrI7Y1xsRxr4mxjA+Bd1ndy0z3LcZ03z1vu+iCdK5/h3xcX4Xn4tXfv8JkFcl9169QK717AD3nl2P1M8nxXANVd3v/kgduQOp3/3+z7XdOxgo7JXv3ToC0Qrf4Tv2EeIW0wI4KoT36xIkos4ypAM52NUpX+AEx6DPwv3XqxubIx2fL8h165pGrsWudtA8SvtS4gXYlxdEY5qN9EiPizjmfbTvElDjhWg9XkJ6pmeQ25v3XuxrXP7zQrqeBxFzSS0kbMmLSI/4XmKbn94r72GI5s4LfZVKAXE5C2ocCkg/v1GZ3oc+ATGbPIU0XO8GrRzzySPJpfsAuT6rDRCNWpEr+gLy+AnRRqUhvtCbkMbxhJDf5fruWW9DfBRvQkx0bwFfx5i2NwmCZxk4HGmM/Rqknl6FHGNe+yMaem+q4i/JnVmnj/99iyGPJsA3br23//tDGrSYG0dfemmunhmHmPwDd0hdel4D3xnx3ZuKxGr1Rnl7WqIwp0q9DPgwYl8dxCevl79cAdP/P5dWBX9aiLDypXunHw6avi+fYxBN513kui+0RzSDt4d0r15C6uGa7rk/i3QchhLjDFQR96YTMuL+c7f9EPBRSNcwlryT0hxEhCCHCJMvhZTfBUjdfqt7F5YRjlUuZ1CY++2CDAZ9BBEms4EHw7gGX551EOH+ZUTxMY0YZxksC0uJF2BfXXwvcZJ7obKRXqN/gEgHpEFOC5B+W6Sn7vmLLSSf2VpcpRB4Wjhk9OiH5Ardfkf0AxDn7Q+AYSHcs69wDS0Srms0ojWYRgyDdLzngWgFD/Pt60NeIaIVIsTcEDCPM1xFdRpuIAViAnyefKZPjOFavE7OMa6h8ruTGHK1xDeH+S679S6Ihm08MmCkD7Fri4YiZr93EC3nNNxAQ/KGYol19huDaE7H4DoL5NV8ziJgHEVyhaADES3zZ4h/8ipyp0S+F5gUY/nz63QZxP3jCqQR/r8Y8qjk3llP63UDIgB/gQzcCU1A9eXZAelcHRqx/wF8A8ViSL82MpjpSaCVb//TiB/jse7ZNwyQtteZOhWpg79ANP9bgBfdsWuBGTGU3/ve0xFLgz/udjNkdrtPCClGpy/tA5BR5K8jGsJWMaTlXcNdwK+FnHeQe06zCGlCBJduH8QisIrc2LJBrXL++rAx4lZwGLkCawOkY381TiAnuHa7DrkDhz1FVBdEWM0GOkaWaV9cSrwA++riq8DuQwbXHIyYy7biQvggZqdA01kivfS7fdufIgLxpYgPyyXIYIuYRgC6SvxtROswtoBzDo+lEnNpVEC0jxciDdefiJB8MBKCpX2M6R+K9NLfR2IC1kP8+05yx8MIg9MVaRC/dA3WBGTwyblII/YuAf1e83u/EL+77eQTcowYR+BHpOU1MsnkCl3Vw0jbl0c6Yu5dgWiFmwZtSPJ7t9xve0RoeR9p2NfiBFNE2zIt1vvkvpPDXGPyDeLaczXSQM8jQMQHRCPknzUr30bPfUOxdhoeQLQ1PXz7qiLCw5/u2DGEMIGEL/1aiGb+O1dnNUe00osJIQKHy+NY9/29hlgE7kPqYm+ih1+JbZDed+SGCUtCNJITgAHePfS/izE8mxVIu9Eh4rkfSq7WO+aBrbipsJHBQNkUUPdHm5b7rYFYzPxlvwGfpcR9n5cR0tSfvrqrMrkd+Jhc7cht2+92z3g64qYwgnwsMgQUIhHh+hnyUTi4OmEtYg2My8Dc0rSUeAH2xcX3YbZDetZVfcfORHqn2cCqgOmfDmRH7PsJGbDxOaLN8Ub/xuyniGgL/kFMTLPxuSq4iiysxuQSZIDGKOBet68S4nvZJmCadckN0dMBEUjeRnrs8yPODWVKU9dodEc6DsuQwVQj3TP/b0h5VEGEoecQX8GXcCNYI9/DAGknRWx7lf1rQGYsaUeRd0dEsz4NGbzTOKy8ECHyCMRc/l/3fQxDNLrTCTh/vK/hOhL4X8R78DhiYr4fp/0oYtqtkU7PcNc4+TXMoUXacOmdhmi2W5KrvfFr61sh1pXFsdQr5PV59zoRCe45rEJ8U3/AN0lGEdP36t8GSEe3jUv/YMRN5lXEEpHhzjsGsWwV6X76ruNQxA3KH/83AdFwf4WY6cOIJNAE6fSMQbScV+CE05DfA+99buZ+L3Z1TLcg1+F7Hu/hq/+QOmwzecMo1iK8qWW966jje9ZPIm1krGMQGiFCtxc9orF7r5YF+c7zSb8+0jHsQu54jUvwxbJGNPW/E2PntCwsJV6AfXnBNxWgq5T9JoP+BAwojmjuNiPCaDtEo7mY3LnRvd57vRjKHhnXsBqiWRnoKq2vER/ZHQScpjG/Sg/R2nnlr4yMvn/fK0eAPL7xKinftRyL+BF9ijRYYQV2L2hQxTmIv/AHwDlhpo9oProjgsx3iEAUeFpOl+ZEIiJJIEJKNs70G+RZFLEMfZGO10/EEPaKvG4SI3z7myOa7kHIALdY44CmI4MWvyFE4QEZNHUa0ul5CxGwD4s4JyxBfjZwaeTz9d1D79kHjgPsS6siop2d767pBLe/DhLXOCcOcBHT94STDoi2ayWiQX+efAQIl9dUAg4GdPXJ1+7b+AARJv2+1UsJOMiFvHWwv4PSHOcz6q4r0Kj1Aq7FH87rZ/cckhHh7q4Y0q6DRJ75hFxT9vPAWxHnfUs4/qM54bnc87nDrXdG6regpnjv/T2JCL9qt/8NQnC5cs/2Bd92GhJ/fQ7SiT/ffUMx+aSXlaXEC7CvLq4CzEacuDv69ofVW6yODKLa5fLp5/YbImaoCZi+V2FdiwinnhCciGh5XnEVfCDfS98Hn4Zozn5yFdd/yJ0d5t/uo6zh/08R8/G0AgnuOry0UxEB8jlEQ/EYAU0o5GqFDkUEioeRiQsa+c4JM85sF8SU/BC583rXRIS7ScQwUMvdh+/z2X84TnMe1jscRVmSgX+HkE4aIuQtiOXeFJK+cd/jePctPofPlzPWb9Gl0QwxXW5yz/he3Ox2YeSBCHaTEQvEHu+qy/9uQppPHGnQP0P8K99ChIcHXTm82MaxDKCa7r6RZKSeXO2ezz34guIj5uiYQl+5dK5w79dniPtVX8Ry8193vMjfjK+O7I7Ut5571zFu/ymI9enCGMrdEd/AS3KFu3eB93z7KxBbxA1v5kLP3/U1fJPUuHOewQ1CDGtBZgVcE7GvyC5FSHvu77wdhCiLro4473Z8kxgELHNzxOLnd/cZinS4vAkllhLSoN+ysJR4AfblBRkYMhExEfjjqsXaqPjnLm7pPv4sQphW1KXpVZAN3Md4uNtOJu9gJJNfoxZlHl6F+BGiDbwZ0Qa+hTTI3r3y/K6CVC7+imV/JGbtZNeIpHnpI8Jx4Mrel8caxAT/A9KLf8hVlNVCSNvrOJyLzHD2LqIVnoo0jk2QHnYs0Rf2QwQh/2wn/Ql5+sLiXNw7WgPRnM9071o3QpicoID8jka0HmvcexZTB8X33Ie77+Iq11C9476diwg4cUBEPlURP83DfPfNr9GrjHToAsV8jriWZESIbOw7dq5L/xtin4nqJHxTe7rv/iokZu5fxKAZdOn56xW/W1ca0klZ6b6jawg4IpvcOriVe5euQvxq/4tobk91x/3xcoscNxXR0s1GOgmH+J71++RGKqkUJP0C8qyJzDr2FdIunuVdh7tnQcNRecqCrkAmuYqVC8j1Q88TmrAIabdA6vPnyOve0dc9izuRToM3O+AZke9JEfNrgLRTHbx0EJ99752ognSCAvtUl7WlxAuwLy0RFbt/1pteiOPzr96HGTD9Ais9pNf9GyE4i/vSfANnfkCE7o8QU918wokFebD7sCv79h2HNPSxmJk8IdirvBrinPMRH9WFSKN4MjEO2PFVHkcB70Q8j0/cchch+REho6O9RioZ0W6/TsBBeRFpjwMe823XcY1JW9++GkEr4JJekEgZmYh58R7EBSSwlsiXbk1k9Lh/RPm1SCdvIQEGnpFXGGqFCBT+OuUYxDT7F+FM/1gJMZWPwzfgz/cNXQz8EtJz+Bjx5bssYn8lRGsbKPKC71o6kxuA/TZgolvviGjyvNBksXYibkX8Ln9FOqXeCO/W7ruf7fI/MGheSAdlqG+7IdKh/4MYXUzctzwKEYoeQgYD5ZltjBAGzZG/y1ItRDnxO7mh1R4PIa8/EKF+Dw12DM+gFtKGv4i0gfchwmsFl9dwpDM0Cbg/hGuohmjn74zY7ylvDnT1SswKkLKylHgB9pWFXG1BXcSU/an78I/1nfM0MrtHkU3MrvIbgZixTkfiwXnaRn8DdiMhzE6B9Lbvcx9ic0RAegjR4L5FQL/UiDwaIb5P6RH7z0FMXYFHMyLaxX6IU3oe/ypX8bzoPvb3iHHAGSLQvYsEyE4kV1BORLS1nxNjrDv3PGoj5svuEcfORYSvWObETkcGsryOE7hcBfyy75wUZGKEUKIJFMeCaCJucN+L5wZyOqKFnkhAfzjf997X3ftpiJvPh/gmwSBG7aBLox4SSaJdxP7u7r2LKQSOL71O7lpecvfIC7dzCCJYhjVo0ptCdh0x+GwXkn4Fckfz34MbgIIIqU+69aADDf2WjcVIZ8ebqvOYiHPPR6wfvxJQg4+M6t4jFifS0Q4j5u8x5EYkudvVhyOQ+L9hCKleR74xItgPQIS+am7/Aa7enBlCXuficx3w5V2TAFOxkteyWBEREG9w92cUMrgpCdFAV3bfqfH/N4ZruQgx/99G3piziYjFLmbFRFlaSrwA+9qCxC39EDE//YSYG/0jZwOFQSJ3fvrZSGzRya6i/95VwI8gZo7Q/FZcY7XLVYpP+Pb/QYy+fojgVdWVfyLQ2ndsEAHnWvelcTIijIxHnPj3GFiGaImfDOE+tXGV/S4iphl1x2Ma3BSR1lPsGRy9PtIDD9zDRgS6cxHT4iRESF0fcU4m8ElY1xKvhVwt4GXIALOfEBeJ98gdLJSCb670IqbvNUbJ7hs8yTUg9V0e64mhI4eM8q/vu44URAs5l7z+lf2B10O+d+e5b+Z7xLLxCyK8PhJyPgmI0LLF1WVBwnb5R933RQTs4yLO6Y2EcBuBaKU9f/dY3a++wE297PL+2a13R4QMv3Ut0Hvm/ns+EkbrGt++Coh7Qcyjy116bV29cgTSKRnq3uPhBAwLiJjyq/m2pyCdrU8Rt5U38M0IRgizKiFm/28QF4wEciNXnIy0yYGiuiBt7vG+7SMQof5DpGPfPday5/NO10IURT+5Z/EMMuPVJ4Tsx1sWlhIvwL60IPETF/q2J5E773I/YjCXI9q0j5DeZzJiDmyECMK/ucr+N2KIb+n7SI7ACXaIMOkfkPQgMDnEe1YT8f2Z4a7vGSQmXVt3vEimeXz+ekgDtgzRaDyAhAbz+5T1iLX8vrQSkekFN7prCRTqqIC003zrjRCT70rEvPwf954FFiSQiQpGI/60HZDBIGMQ4bunL9+NhDSYJt4L0hH6CzjRbddHfMl+IwbBISKPE4Bx+ex/HbglYJpJ5E52YHDRF9z284hg/C0iRKzBaVkJ0R3D1S+nIQLYbYi2PRbrhlevpCIWp2a+fXWRsD7ZRAxM2dvzdb8VkE7aWy6dX1395ffjPx+pfz0fzJjj87pncbX77tfgZhtDLEFPemUL8t7ms+/f7hufitSVY8mNhBIkXFRtoIlb9/xQ/4MIk83cc+rl7meRJ0Fw6Y1y9+IgxG/7Ry8/9249gfh3vkpEWL0YnkkzpGMV6VIyliLWj773cxDwU8Q7500c0h0JP/c54c9AVd39Hop0IsYjQmsf79mVp6XEC7AvLYigOsGtX4ubgQMR8GYScG53oJP7bYXEVnsHp5lFhIk9HOtjvI4vXcNxi29fImKSn0nAATvkms3aAFcirgTHIfEnT3UV2x2+Sj+I4/tc3OAit90TcXTPdJXn3YjW4GFgdEj3yz9XfU0kNucuV0HGEuC7HWK6/AXp1T+AaIGTkIEa0xHNREzTMyKN/e2Iv9j9rnLsiJjqPkeEgBXA4DDuV3EsSAP5ReT9d/fwlRjSPZzc8DpNEevGZeQNH3Qd8EEI13Cu+w79fsNtkMbxSnJnPwsjwLv3/h4MjMrneFBTuV9IfRsxl7+DCCpH+c7rSBFmvfKl+yLONxzRpL2MNOzjEO1aaJ3RiPwvRMzl44F3fe/cepwLTlGfi++aKiPawXvJnYq1FdJpeBfpcFcLkof7z2RXzteR+vYRcidW+YjcwVOxxMrNQNqRWe46XidvJIyGiND1JgEnQcnvnUSsgJsQofsBd79mBEkX8RVdCxzoO3YpcJ1vuwHSYTm6oDJFmadnPTkZ0f5/j9T5V8Tj/S1rS4kXYF9a3Is9xr3Mq3DmP1cpjw+Y5rP45jxGBLvJiND4X18lGcosPr58eruP9Hd8g7NiqFS8SrgZIlz/j1zfvscIyTEccXL/HInveie5oW5qIcLdO4gg+2cM1+JVKschGoH3XaXoD1x9GLFpORsgZut3kNA3DyA96h/whWwi3BmC0hA3kp8R3+S2iG/ns8C3Yb5f8ViQzs6Rbr0aMsDhsYhzurh7GCSKRC3EfJzu23cTYpq7DOlw1UU6c1cFvAZPYGyIzKR1smuw1uJCkcXp3nmN81fkDqCMOQyZ77t/y73LB7nvch3SketPwFntEN/5LfjmuEcEoh8RQe8NZKKSmGLkFnKvnkE6cA8ilq5PyZ2oJMj75aX7NtI5/QxxWxhJSFPXuvrxK6STOw3Rmg9E6uIP3bcf6+xmfreHni6vHUjnqk7E8UAa24j82iFjM3q49VrAo4hy4vIY3q+hwD/+60LcMLzwYKFYMXzPPQGxlF2DRCbphbSVn1COQlHle49KugBleYl4wbyRpJcio+LnIWE47kFMU80CpN8I6R3u7+Xjfm9ENC07CMFsUtgHh2g9dyKN8YEh5PUx8LBvuzNi0voSMafEEjvRr9U6DYko8BdwiW9/U1eZNQnhmXuN1G1ID9gLuB/GfRqDaGb9lXoNZEDebnJ9LUOPaYpoV35AGt4eiMAUl3BOIZa5ChIr0+/rfDwiDL2CaKHaI43mbQHzeJsIn1DXeN3j7tVUxO1jWAjXMwG4x60nIqbrDS79o2NMuyIyCKgfeWOxdsAXc7KweqGI+R2IaJ49/9Cvkc7PfxGB9YWA6d7i6sG+iGtHE0RT6O9IfEZ4U2bWQwSIAUBvt68nYnl4hLzxsosaKso/6Ggq0mk0iCb1C2RWtZhGxbvnfg2ivb7Gvc+fkTfkYFrQOoWIgUQurX+593cwohwYgQzgikkx4XsmF7r79QXSMZmFE+qK+gwirwWxxI1FhMXLEbeLTO9exppHPnleQoQrkXunvyPGqdDL+lLiBSjLi++DvBPxT6zuttsjJqkXEIfoIwOm/y4F+CK5/H4mhBH+vjTvBM7LZ/9rrgGINb5hEqJJ7eG/JvcxfoHPHy/G5+EXWO9AtBKTcabSGPPwyvwIbsCXq5C3IkLqYkTIOyGGPI7HN72uq+j9PnePAF+E9dwLuk7EB2sDcEM88wqpvJnkDVCeiGjvz0IsGvMRc+BjAdPvCGyK2PcuTnOKjF4+ABE0gg7a8DT1tRDXFG+GNuPb/wYwPcZ79RIygHEZ0tm92HesofsNU1PfEdc5QDo+C916C3cPA3e2EcvPTKSjOx140XcsDRGEWwRNPyKvz91yH6JAGFnAebEISDfjm0HNt7+H+xYDD9xBBNMX3Xo1RGB80NWN/yPgwKnIa0eEuqqIhvZB37GmrgxzkfYxZi0xogTyJiJ5nFzXu47EOE2qS6cOYi2ZhnSKLvIdy5n9KqT3K8Pl09L/HrnrejisfMriUuIFKKsLuUJRW1dpeS9XGiKoxjRvvHtps5HBHzchZi6/AFYX0eIsIcbQRy696sg8yN8hgmkH37E++EZoxpjPA4i22a99SER62x1CyuNh8voRJSODT7IQH6xAkRd86VVC3BVOctsf4rQdiMbgAWLzTZ3uNVbkjRjhCTK9Ed+4tDArygLKUpEQAsrHuYxtgG0R+17CxSEkVzsVeGSxe64/kjt4qSkyiCbwNMWF5PUzoh32RpUb8nZUvAEwQQbrdHJpN3bbjyHWkoNcHVMHseSEpqnHNxEFon18yq3fjOuIx5i+QTrZixHB1/MXfI9cDVisE5OcBczy7V/g9nlT3MZUp7g0myF+9NuQzmjtiOOx1CmtEUE3JWJ/HaRD+jLSwX4ixnxORoT5j5DBl3uEskPcid4I4X41Aca69erue/yX237N+35CyCcB6VTdhbgTfUxAd4K95FMf0XA/7da97/wH4Nqw8ytLS4kXoKwviInWaxBPdC/az4jAF7gRQ0avXuw+/FmIBuQCfD4+SAMcmt8aYlru4j7yHxDT3CmIIH5awDQruIbEM5VURUwobyK+URcjvesvQ7qGaoig+LWr9E/0HfsX8L8Q8jBIB6U54kv6E7mxRz8hxnAlyICvBYhP3/FExABEfDG/IcbO0L6yuOe9CTfnuWu01hPS6Fj3vC9GOjtvI9aMX4EB7nigGW8Kya8nYs5ciy/OKL4YvTGknVNut30GMvDvS8Q/bgKiZR0Uy/3yrVeKONbVXZc3HexRQfPJJ99GiD/nXPft/03uwKDAzwcRVPp5dQeihfzOrTdHhLKYYgv76vQOiCvRB66OvIBwohR8jgwwq5xfekjH62qCu8Wk+NaPRVyufkNGzZ+CT0NPRLzZGK6pMqJNvxKxqDzn9h+EdMbSwnq3XLqVkMGUr7hvJvDEFAW9k4jb1VREcfMB4jv8VZjXURaXEi9AWV5cA/UU4it1EGJevBkZNf0uwU3+17Kn6fceJH7pO8ggnrQQyu83NTYld7R9RURr9xkihA0uYrr+2ZpeQjSEt+KiEiA+a/2RgQ8L3LXV9641hOuqjXQankIE7qeIcSYtChEQkNBOTwBDgKUhvVv7u4ZqEeLU35bcRvcHYGBxveelfXHf262IsPUuMvhosDtWMb8GIYZncod73ssRS0NM7ip7ye9OxKXkO6BNCOk1d/fmU1yILmRgzTOI20I1ZEDYwcQWk9fzH7wAEe4/RzrAnoboGsSceUac7lsGov2+wm2HMSjsMEQgbYoIQV74vDdxfsuF1RFFzKsy0hl9FLHWjCQGv3ckekQW0rl9Aemc7OFzjrRnQf1TpyNWh1pu+zKX75uIZvt2947dQcCBxQXk2wUR5nYjmtr93PbgENLO93kiHeEuIV7DVUhbeAm5ESO6ImNRTiYEF4ayvni9OCUgxphTkApsAvCDtXaw278C0axNC5BmItIALjfGVLbWbnf790d684cjH+Nt1tp1AdJPsNZmu/VkRCtcA+kl1kHCHb3tjlcBdlhrdwfIZzHSCCYig8zeR8zyzZDpGK0xJtFL2xhjbIAX0kvDGFMLCVLvXdv+SKzLBxA/1f9Yaz8qavoReXVB/MVmIya64UiQ9HuQWIcvW2u/iyWPiPwyEJ+46kjDlYZcR9Ow8tgXMMakIRrz05AGchzwkLV2kTse9N0ySMixbGvterevLTKIowWiOZqITISwNWDZK1hrdxljDkbMmc2BV621W40x9ZHn3hvRro4KkofLpyISmu0URJhLRgZ51snn3ED3y/f/eshgw+sRIa8LIigNtdb+HDTd4sIYk4q8Tz2QTsmfiOm3LVL3dkXegcGI0LreX6/uJe0Ea222MeY0pPOzFdECz0A6CGvceVWR+3YScLO1dlvAa1mC+I3ORtqPxkjn5zNkooItQdKNyOMIxAJ3AKKEGObq5P2QDsuRyIDH9oiQ92uAPGoggyJbIpasYdbaScaYbogbRntES/+dtfbOWK/J5WkACvoWYqlXXPt3F9J+TEfqk1VI5+QDa+2GoOXe5yhpSbmsLeQ1a/nXm7jfSojpfEyYeZJ37u8TgTcDplUVmQbOm2LwBSR0zP5IYO8bEM3wrQQLseJpU+4DPvftPxoJufIB8lF+h2+Edgz5+F0KMpEGfX/feUmI/9VgAsaZ9eV1GzIg57+Iu8ICfGFDCHEASj5l6IvMFJRNnDRRZW1BhPbj8IW4QcI6nYkMOvKiMASaGQzRzGciAtbHwAURx3sgFoNvgrzL5J1TvQ2iPf8ScR3aCdztO36Iv74JkFcLnHkasaCc6r6L2e7biGne+Hzy60Le+K/NkY7qQvf9lNp5ypGO9SuIy9XniEvEt0hn9L9I5+RrVwd4A3mC1JWT3fc8AtHSzkVchz5Agvyf5e6bFzM7SMzUK3EB6337zkCUE2MRl4YOQdL2pedvmy5CXC7m4XOBcu93J4LH4G6ECHDT3bc43t27d8kdxJxEbLFf/TOd9Y/4PsN07fHySXDf4KFuuzZilZuIyBBnhJlvWV5KvABlafG9YMnuJXoEuN53vBISkmoy8RlkEUZg786uIvkc8YV7nIjR8IhpbjzBp3ut5iqR8337BrnKqyVioplKPhEGAuQ1AdHWNnSNywRkUFgXnHkLcZfoHWM+lZDBGl6Q9WG4oO5Ix6FxMbx/lQlpVqWyviBC6reIa0o2vil+3fH6iHA/iYDT/SJCwxtIY56JCJGV83kmRZ4uFRFAngH+z22PAu7wHT8ZGZX/BDG6w5Abk/ccfAMv3TfTm1xfuFgnjvA6dMnIiPIPI8uOaLxjHkAV53frv4h7RyvfvqsQwfUDxPoUc/2OaBq3ufzqIkLlhYiP8J+IhvX3GNI35B0f4PcTTUJMy18jmtVAMaV96UVOrDGU3ElPwgih+BkS0szrbCUjI/unIe3Kgd41B71XvvX+iGbzDffN+I+FKbAOcu9Tt4j9HRAr7R7TcZfXpcQLUJYWcgXVTFeJvIiMup+JbwYUYgyYHG05Yvh/RcQE9Cdi1noX3whTV4nNxU05GCD9tq6R+hqZlaSxy+dfvnOGETBUkC+NsxAhJdtViIchfrFvIabf11yDsyyEe94A+Mit748I+15829GENMJUl6ifx/uI20WSa7BmIQPP+iECakN3LKgG53hgtW/7IPfND0f87IYj/uh3RjbSUabfEelUjUWsF68S4dPuGsnPiHHGOUR79gK54a4SkY5Xc7fdyF3HoyE9m88RoX4X4l9ZNYx0i+m9OgAZrb7H7FJIHNU5uKgFMeThT/NCpBPhdViOcPVyO8TlpKb3zALkMwSJg+2fhjdPSCVXN/cL8f75B1U1cO/3dvd+B7I4IZ2bJeQ/+OhARDEUVifLm/3rPUTB8QnSoQzUFhaSn8FNKIBY6QJHJCkPS4kXoKws5AqphyFhMLyBLd8g2oidyMCE5iVd1r1chz/MTXPE9LDcfTQdkQDTVxNDT96lnY44h7+PmM5+9R1LRExdXhiZWMxOdyAaif+5hvFVxM+vh6uk78BNQRvj9VREzE73uYbYi/RwLOKLpyaaYlrIP6bpr4hJ8EfEdBZ4VjCX3nR88SwRf8XdyBzvQ5GwTt8SY4xZRFP3NaLBGUdeLV6qe7cCxwFFzK1/Rezz/KnnIYNdarm8Aoe589WPZyGz2bVCrDdfIxrCO4KmXczv1udETNiACHfewNP7gI/desyDp9y9H464EzRHBLsHYk0fEXiXIa4xVSKOJYVYfk/Aa40Ixi+7b6Od75wuxDBdtbsOf6SKCuQVtl/AhSGL8VpqIj6uTdx2XWRQ2CxEmBxIOLFZ/R2VA5HBjbsQn/oS/wZK41LiBShri/sI/+vWewF/uvXXcLH1SrqMUVxDZK/6eKRXvxgRwh8jYPBnZFTsATiTE6IZ6I9ojz5EGvzHvYoraGXpazgOQEw0F7iK5hNkcMIthBT/ExdhwV3LWJf+BYiQOo2A02XqEvh5eDFN27vt/YDN5M4Od7k7HtiyQd4QYYe453xzPucFjc+ZM7MN4l99u2sMMxH/xB6IC8uH3nkB8+kODHfrKYibzA7EF/1ixB82ZhccX34P4WaCI9f0fC4yS9xaAvoLF9N7VQkR4L91Za7nO+YNPO6GaNuKfB2I5vy/BRx7xd2fX1w5YrWa/QTc4ttu5J7NOGRmLW/2prAiYvyKaM8HI9r0mYjgGthn1KVbDzGDL0KsgH5Fi+e7eyXS4Yr1nrVyz79RxP5bEGEyE+gfQ/oFdgTde/UbYh2MaSa1fXEp8QKUtYW8YZzG4+KYIv4mpdr8S27vtx0yf/zLriHzwsb0QWZy+U/AdE9DzO63kytIJrnG6ihEAP7FfYzeDDhRVy4FnYtoD34ArnTbXRGheyMBhVVf+c9AtMKN3bX8HzJA50tEg3dFST/X8rSwZ0zTG9gzNmhLRMisHmNe+7vGdwVO00K4MzalIJ0sLzTbYa4x/BURvB8kt5MUVCBui5he73PpTcc3wxxi1nyccLRrXRDryWwiNE+IMB7IV7gE3rETENend5DoCFXJFVQ/wvlDF/WeIRpTL95y14hjR7i68esQyt8cUTx4Vr86Lu3PEYFuPiG4efjuyfVI9AAQa9nfSHSXeYg2MtYpbGshbjCT3HO5NOL4NEIIiI/4vU5EIm7493dzdc357ls6LkDarZEO9hPA6a4dSffy9Z13IyHONrmvLCVegLK0+D5MT/h6ATE7H4lo2Q4v6TIWUnbPNFcT6Z0Oc5XuTPcBeR9NKgF9yly655A7iOl4xAH+WESLm4JoIs9wx4PG7LvHVRp9EeHBuMrkUfLO9R54GlNfGkuAPhH7PF8/DbhfQgt5Y5quwA2oc8fGE6P/c0ReHRG3np8RH9jGhCPY7Yd0eMb4vznXiH0PnOm2Yw3y3x3RsH1BxGA81/j3C+k+NUd8Lr9EtMM3EkJc5BJ6vyq4Z73ICRcNkAFi/kkEgmq5KyMC1ybyTl/bBnHDejDGsldCNKf3Ia4YzyPhAL3jvdw7lxbCfTLu/lzitp8BRrn1txA3mUD+l+wZ2aU+MkvjHPfeNnfv9sIQn3sbRKifj3SCr0TcMi5wx98hwCAnRHmTjXTiliN+tevcd/4aMjD7AiK0ubq4+1fSBSjtS2Efs/vgv0BGsMfkE1eM1/MK8Ipv+0DEXD4LZ+oqSgVMrgD8ML5wVG7fatdADkeEy4YRx4tc0SOhh7IR0+47SLibZ13FlY34DIdl8u+Bmzs64lqrE+MoWV0CPQ+DaFdq+Pa1de/e+66yHwqsjFP+fRHz3E/kEzA9YJrJiLD9LL5BU4Q4hak/L996IhJKaHEc8qnrBIrxiEBUqi1Ne7mWNETLPRURLG8K6/kggvBGRFlwpNt3GW666iD1oy/t3oiLwgpEKXGI79g5wLSQ7k8CYjI/EIn2Mg038xSivb0ohDzews2MiCiJ2iCTuCxxdX63gOl6VrMGSJSNjm67BtLJmokIkd4zT0FmvOsYIK/aiGLoBffNt0LcMWa6OmWy+61e3O94WVhKvACleUHMvp8jDun5ahkRTc7zlFLtWmRlh/R+r4/Yl+IalaAjpKsgJpOuvn09gbfd+q2uAotpalGXViLSy12M+EP9H2Kq64D4wp4Yax6+vA51FUlr/71078NXRIQq0iV+C3uPaXo6EtN0VeSxkMuRDPw75DQPcd9P5HcZykxH+eRXAbgOiWIQaGpkl46n8WqJaIyeQaZGbesEiiOdQPF6Sb8/Idyzg4GXYr1Xbt0/OKuGe2+3I6GKQqtTXJ14EHsOpppPhJWoiOkWNkvfK8gEKzcjGsOgLiveu3U70kmoE3G8IuJqUiQ3tfyuARn85yk6JuCzjJJ39sbhuMgvRcyrk/tthUR0eIdc/9qFwKluPaboHvvyUuIFKM0LIhTdivjaPIb05CpGnPM7McyLHceye0JVIuJj1c5tn4v0sk+JOH8+LkRKwHzGAFf79jf2Vy6uAYtphHREvmmIf98UxEcqFC2qS3t/RDBq5yqnGxAtqmeCGk8Z0aDvKwvRxTStRABtRzGV398wdkI0KxlIZ6gaMkJ+MRIpI88EH3EoSwVEmIxprnJfer86AeVaxFf8Y9+x6rgQS/vKQgzuDIh70hvuHT7Pt78D0ik+LoTyFeTL3xZxVfs6pPvQGdGc90VmTQOJwf0bEuIpJk060tn5mVxNp2fRqkCEdS5A2n7/2rFuvbqr73cjUWT8k7k0Qax5RRImEUvJS77t4xDt6TnuWbwb6ztVHhadQjUK3PSMTyKaj1eQivgPY0wf4GFrbb2SK13++KZoux/ROiYgwugQpDFsivTm1iEmiGOttYcESL+ytXa7MeZlxP/1MuummvSdWwXxyTrNWjsz1ukZI9JuhYSk2oEIw99aa9cGTOt0RDN3OrAB8VVqgEQq+AERJBoiGoqjYy68EhXGmOMR7Xxdt30QYg6chfh8tUYGCVVBQrxklVRZI/F9Jw2Bv621O4wxlyPfZHuk/P9CGq9DECG1h7V2fokVuggYY3ojHdAj3NTPy5FGeDLiR/6xtXZTSZaxpPFNmXorYtp/C9EIHo34Pz5hrZ0cYx4Vgd02n+mofe9gDyRs4CfW2sUB8/Gmqz4XiR6wBGlXeiNC9jxjTDVk2u0dMVyPQb7n4cAb1tpR7v3C5f8B8Ly1dnyAtL3nUQPR/n5lrX3Pd7wNMoiyNjJ2Y5dXpqK0W8aYRog/bRtr7RJfvjciAxizkMglvxf1GsobCSVdgLKAtXaDtbYPMifvecAwY8yZiPB6QwkWLV/cB2GNMScirgl3IwL2v5DGcDBi9tuMmOgXIU73UeP7YIe4eb2fRUx9TxhjGhpjKriyVEZ6pz84ITUhLCHVlWMOuREF/oeMCC8ybk7qF4A/kLm1ByAV8EKkspyOaNafR3yTleLjccQX3CMZ0Q5tRhqTlUjnYmNpElIhz3dyOzDRGNPdWvuytfYCZJT/3cj79jMygchSYJwxJupOY3FjjEnwhAbHAvf7X2C6tXYS0nm4ArHolFuccJPt7lcnoKe1djDSdjyIhAN81hjzlDEmlvb4WeBiY0xt2GNueuN+ayLPJ5CQ6tLd7VbvAgZba09HZtf6wwmpxyCWtMBCqsvHWmu3IiG7bjbGpFprdzsh9Qikzv+i8FTyYoyp7BQr2W5XV6T9G2SMaeDLe5a1ti0S53uX15YFaLeeRMZtLPHeA5fOk4hlaDbidqfsjZJW6Za1BfnouyOj/L8t6fLspayzyWuOfwQRUF9HHOxHIUJAjwBpe6P4fwFuc/uOQYS59UijOwKJOzqJ3PmY42nSrEjwcFQfA89F7EtGBN85hOi2oEuRn83dxDGmaTGUPxGJfHEPEjZoJIXMdIP4dfYr6XLnU66qQOeIfYe6OuBcxD841e3/BHi6pMtcWhakc/tV5HuLmJQvx5nJCR5JYKjvG8kgYkwFor3NIgY/SHJN7w2QGdNquTpyNbmDwV5CrGph3bdqLq9ViMXsSdeGFTksIDLospNvux4iqI5FXMhuwU2GEEK5MxCf198Q94jm5PVTrovEZl1CDBNtlJdFTf8BMcYkIS/YxpIuS344je/7wOHAfGvtJmPMr4gf1O+I5rATUnkNtNZuC5jP+Yj2sYu1dqXTHFyOhItagwip4621yzyzUazXFjbGmDrIvbrCWvu760Hvtu7jMMZcjGihW1tr/ym5kpZfjDH7Iw3NcTifa2CFtXZniRasCBhj6iOm14eQUDvjkHiWf7njFaxocAYCva21B5dcaffEGNMTMVt/iHSAV7n99yCN/GoklvJFyEDU1jZXe1VucWbse5EoIhsQgWuKtXaZO17BBjQvR+TjvVtHIgOzMoF5VtxNpgOfWWvvKGrZ8yuPMWYcIkA2RdrBy40xjRHLwMHeu1HEvLz3vxFi/atoxeSfgNy7yxDB7ztr7cdFTDsRuMZa+4zb7gLMsNauNsY0Rdqrbu7016y1I4pa/oj8fkX8kVchnYj1wHOIFnittdY6l8Ie1trXY8mrPKCC6j6K+zBvQKZofBsRSC+IbPz8lWTAfAwSwuNba+3LkceCVrrFiet0fI3MlvWwMSbJWpvlri0B6Q1nIpqCOSVY1HKPMaYj0hinIe/dh8CS0v6eRfgMrkLK3hCJpToGifvqHb8W2GKtfa2kylsQzj94KBLO53Fr7W1u/9HIwNOaiHXiW2vtlBIraCnEGNMSGQ3fCtHgfYoIS+tCzicDiaFaHXGJqo/MUlU3hjSfRWY4m+y2j0NcPQ5GOid/Iu4HU621/WMoPsaY35EBv0cgPs+3WWs/jCXNiPSPRKyKnyDv6g9I+9gBsaBttdYOiCH9a4G7rBu74triu1zaU5H79ou1dkPwqyhfqKC6j2NyB4Kdj3ycNyDPPbBm0BhTE+m1pyPm9ouRqUwfQLS0W4AWiO/SdbYMOIsbYx5ErulE16v3CxbtkEblIGvtlpIspyIYY/oiJrVtSFi01SVcpD1w30kTxAVnp9OiPIponE5z79UJSKDva6y100qutEXD/D975x0mR3H04beUc0IgBAqARQ4iCDA5BwPCGPjIQZiMwYDJGUw0yYATOYtooshggskm5yRAILLBIBBRSP39UT3avrnZvb3TnfZO93ufZ567nZme6enp6a6uqq422wBvVwbgbgpj4/4+oZ1PnoI62sG+eFv4cQjho3hsA1w7OBR3j7iyhfKQfSML4bPyb2zidX6Br8Y3FBewzwwhfGhmo/CJuevgvvz/CSEc18R7ZBONtsZXTlsz7j8OX9jjIeCwEMLTTbx+p7Rdj4L2Pnh4sPtxgfVlfALX1OAThDs0xSIQBdM5Yxl1CyH8EPcPw/2Sl8WX6T24uQcpsyoSVNsJZjYCD4vyHT6ieyiE8EUTr3UWPhGkM+4vNBAXUP+HmzcG476qnUIIl85g1mcK0dx0Hy54HxRNTl3xhuxW3Gx2dC3zKOpiZt1xK8HFtc5LEWZ2LB6u53R8MmMvfFLe0iGECfGc3vjM4lfj7zZhhcgws4OBI/GJh3uEEJ6qcZZaFWb2BPAz3j5eh2s2M4F1X+DulhzIx8msG4VkVnsTrzMP7me9Nm5hOh+Pjxui5Wm20PRoK5nw2APX1r8UQvh7crwfboH4FR7qrNHKAitFKzgKX953r7h/ZzxiwZe48Hhpc7vzxfKZPpnK4iTnEMK2zXmfWRkJqu2I+MFsiK8YclwI4cwZuFbnkMywNrMd8biAGxSc26SR6cwmjngPwSc+TMbDUfUCXgs+S1uIRmEevukQ3F97AK512r0NCqSZO8y8eJzhJXFfvq+ij/cf8YU49k6FjPaMme2BW5vWxeNK/x1fjvfkEMIfa5m3pmI+q/8uXEExCQ8RNa6Zrv0rvIw648tsP507Pjjz527kdTMhdWHcujEJD5X4WDw+AHfJWBq3zrTId9lW+sHWiATVdoh5zL2eIRfvtInXykwq8+DmmZNDCOfmBdm2QtSizgesj2tCnsAFVZn8RZOIA8Qj8MDo/8FXh3u5rZjIE43XAHyhhQ/w6CcbAOuEEP4Vz1sKD1P0Vc0yW2NyLkO/wZfaPS85viEe9/kHYP7QCicDJmb46bFZk31X4f6cJ+LuC6vi/qljQhNCUkWN5nshhPvMbA58EtX2uLvCA8AZIYRPZuBZ0vfxML6sbCd8AtjxlkzwNbMeIYTvrIUn/UpgbTwSVEWzYb4AwkYhhM1rnRchWhvRveR03G3mLjwcWqufnGdmK4UQHjWzK/GJJrubxx1+Ew/xMxT4PoTwbk0z2gpIBLp1cZ/3jfBA+N/lzlshhPB4axZazOwC4DF85aZPzGxZfOLf4Ci8DsZDEnYNIVzRhOsPwUP/LRRC+DDZPwj3e90Ynwh2ewjhTzP4LL8BLgohDDCzdfCFb9YOIUwys98Cy4QQfjcj9xAthwL+i2Yhao3uAVYxs/VrnR8hWhshhA+jC0m2MtXCNc5SWSwGnzef7b1j3N0NdxsCXyLzzBDCN/iEsD1neiZbGYmQ+ks8NufiuPbuVfPViKYTQng8/m11QmpsywG+xi0B50Rt+bn4ym9To9bx4xDCdfiCKE3hXNx95MN43z5m9pvgoa2uwSMX3IPHHG3sM6xvHk4tYzjuiwo+IczwWKfgFo6bYjpDtDqkURXNipktHxSWRog2S2Lqz5aAHBl8yejT8MmYL+NLR88Xz38TODIKLe0eMzsTeD2EcH4sww3xVbp+Bk4NTZx9XwvMY7OeSil+8Ug8HNwMmcYttyxy3HcLHvP7wNy502fON+L6++Chvx42szkz94HEX/UAfLGCbrg29VdtzW+8PSFBVQghakBr7RgTQfV6fDWizeL+1fDJLgvjSzOPw0P8bBRCWLZmGW4FJNrUpfCVxa4IIVwQj3XDw5TtAQwMIWxXu5w2DSvFL+6Lhzm8mRmIX2y+AMFrIYRt4u9lcLeCeUMMnWgeNeOREEKjlkqNabP30QN3s3kT1waPj8eXxsNS9QaGB1+QptW6YbR3JKgKIYQA6nTwa+ATp17Hl8W8KYQwIQoUZ+Am7YBrXM8IIbxRs0y3Isxsb2BzPETfKcBV2SQjM+sDTAkhfN/SE3ZaCmum+MVmdgw+aepZvJwuwBcU+HM8PhIPFzUsNCJcVCzjHfGJWK/iEQQ2xxeomB8fXJ0dQvjWPHb2RyGEv7TV99FekKAqhBCiDmb2Mm7yzZaAnAT8Axgbta0jgc/x5SAbPdt7VsI8IP4WuHZuMh6L2XAt6k/ANVlkhFkBa6b4xVZ/WeQlgYnBw6A9AvwrhHBMI6+5PnAm8DSuSb0v+DKpw/EoFaPxQdZFIYRrk3St0rohHAmqQgghpmOVl4B8Hp988kRrDK00s0h8HXfAJ5JNxX1Qf8QnKX+OL+4wFA+19BhwrISh+ljdZZHPwQX+A0II8zTxevMA++HRNV7A3RQeBb7HheGN8dXgdggh/GcGsi5mEhJUhRBCTMcaXgJyOTxm8iGhiavbzQqYWSd8IYft8JXrpprZoriZeT3gYdykvR0eN/duae7KE90K/gAsCGwaQri5kenzK0CtivtQDwQexM3+z+MTqBYObWjJ4vaOBFUhhBBlKRAAtAQkYGaHAyuHEDYws66pC4SZ7QT8FVhDWrvqiRPPRocQrp+Ba9SZFBXfxTa4RvUufFDxzgxnVsw0FEdVCCFEWYIzLYutGkK4r70LqZEfgGzVpClQisMZQrgEeIoYKzcrO1GZEMIPTRVSoyUAYLCZrWJmm8ZrXgL8H/4+fk8pfqpoI3SqdQaEEEK0fhKNqsL4OO8Bu5pZ7xDCN1FQmpZoV98H5oXWGdh/ViLWyanmy7DegvsM94khrg4KIdwNHG9mNwDvxjRyw2gjaJQnhBCiaiR0TecpfIb/TWY2ewhhatQ+/2hmcwGbAdeCNKozgUzgvBB4NoSwPD7JbTFgnJndZGYLhhBexTXhSEhtO8hHVQghhGgCZjYCD4A/HF9edhywNrA88F4IYXfF6Jw5mNnCwG34SmqTzexZ4CLgY+Cf+KpXC9Yyj6JpSFAVQgghmoiZDQG2AnbBA8x/iAutF4UQfparxMzBzObEtdgXA2viy/wuZmaz476p/wghfGRmnUIIP9cyr6JxSFAVQgghmgEzmxv4bxZjVn6QLU9axtFPOOCrU/0GX4jhQGDdEMKqtculmBEkqAohhBCiTZETULsDfYH/hRB+iitRXY4LrQsA64UQXpIbRttEgqoQQggh2hTJ6mCH4uGn/gu8CtwTQrjLzFYCBgCfhRCelAtG20WCqhBCCCHaHFFz+gqwCa45XRBftvZ54OoQwls1y5xoNiSoCiGEEKLNEaMubBdCODb+nhdYH4+6MDdwQAjhxdrlUDQHCvgvhBBCiDZBNms/Tlz7JbCemd0PPBJCeBf4h5k9BywuIXXWQBpVIYQQQrQpzOxFYBIwEo+feiPwZAhhYu48RV5o42i1DCGEEEK0erIVvsxsY+D7EMIqwNLAV8AfgP3MbAMz65WlkZDa9pGgKoQQQohWTwhhmpl1AeYD7oj7xocQ9gKOwCdUHQJY7XIpmhuZ/oUQQgjRJjCzDXBT//fAqiGEZ3LHlwkhPKNwVLMO0qgKIYQQotWSmPwXxQP7DwHuAp4wszPNbEB2bia4SkiddZCgKoQQQohWSyJ0ngYsF0L4KISwGbAmsAHwqpntZ2Yy+c+CSFAVQgghRKsk0aYOBz4Ers72hxAeDiEsBJwKbKyJU7Mm8lEVQgghRKvGzK4ANgYewgP5F646lcVZnamZEy2KBFUhhBBCtGrMbE5gB2Az4GXgVuDxEMJnNc2YaHEkqAohhBCiVZEG6jezziGEKfH/BYCDgPmBp4BbQwgP1y6noqWRj6oQQgghWhsGYGab4suiPmBmhwHdQwi7AmcBqwJz1S6LYmYgjaoQQgghWg1m1jGEMNXMlgduBo4FhgEL4gq2k0MIT8kftX0gQVUIIYQQrQ4zuxV4MoRwYvw9B3AcsBEepurj1EVAzJrI9C+EEEKImpPFQTWzTnHXq8D0wP1x4tRewIvA7HGfhNRZHAmqQgghhKgZSaD+Oc2sa2LOfwvY38x2MbOOcV9vYDlAJv92gkz/QgghhKg5ZnY2MAo4I4RwY9y3L7AOPrnqY3zy1P9CCNvFoP9aKnUWR4KqEEIIIWpK1JiuBqyBz+afCJwWQnjBzFYFlgVWBi4D7goh/CBBtX0gQVUIIYQQrQIzGwwsA2wKLAT8CzgrhPBF7jxNomonSFAVQgghxEwn1YjmBU8z64MLq8sDCwBXhRAuqk1ORS2RoCqEEEKImmBm8wJ3AM8CiwGPA/MBTwNDgX64sDoQWCqE8EJtcipqRaeGTxFCCCGEaBE2wwP5/wBcDXTEl0ZdEngOn1x1GYCE1PaJNKpCCCGEqBlmNho4Hp9AdRlwawjhpzLndgwhTJ2Z+RO1RYKqEEIIIWpKjKV6PLAt7gbwF+C5EMKkmmZM1BwJqkIIIYRoFZjZMOBkPBzV3cDfQwiv1TZXopZoZSohhBBCtApCCO+HELYFdgXWAhaucZZEjZFGVQghhBBCtEqkURVCCCFEqyT6rop2jDSqQgghhBCiVSKNqhBCCCGEaJVIUBVCCCGEEK0SCapCCCGEEKJVIkFVCCGEEEK0SiSoCiGEEEKIVokEVSGEEEII0SqRoCqEEEIIIVolElSFEEIIIUSrRIKqEEIIIYRolUhQFUIIIYQQrRIJqkIIIYQQolUiQVUIIYQQQrRKJKgKIUQ7xMyCmW3XwvdYPd5nSEveRwgx6yJBVQjRLjCzB83swoL9Q6IwtfrMz1VNGQz8s9aZEEKISnSqdQaEEEI0D2bWAbAQwtSGzg0hfDITsiSEEDOENKpCCBFJTNXrmNm/zew7M3vVzNbLnXe4mb1jZj+a2X/N7G4z6x6PHWtm43PnrxyvO0/8PcbMfjaztc3sFTP7wcz+Y2ZL59ItY2b3mNnkeJ8bzWx4cvxYMxtvZlua2evAT8BeZjbVzIbmrrVlvE+/+LuO6d/MdjGz1+I5X8TnH5Icr5iXeM4+ZvZBLLe7gWFNeA1CCDEdCapCCFGf04GTgJHA08C1iYC3KXAosC8wP7AOcGcT7tEBOBXYC1gO+Ay43cx6xPssAjwEPA6MAtYEpgL3mlm35DpzxWuMARYBLgc+BvL+p9sDt4QQvspnxMyWAc4FTgYWBFaP18mON5gXM/s18GfgTGBJ4DrgtMYViRBC1EWmfyGEqM9xIYS7AMzsYFzIWx64GxgOfALcFUKYArwPPN+EexhwUAjhoXif7YGJwDbAhcDBwG0hhGOmJ3AN6JfA+sDNcXc3YPsQwvvJeVfGPJ8cf88BrAdsUiYvw4BvgZtDCF/HfS8lx6vJy0HAtSGEM+Mpb5rZwsABVZWGEEIUII2qEELU5/nsn+jLORUYFHddB3QG3jOzS81sezPr3cT7PJ7c50vgNVwrCrAs8Jtoap9sZpOBL3DBdP7kGp+mQmrkMmBhM1s2/t46pr27TD7uBd4B3jWza8xsNzMbmByvJi+LAI/lrvtIpYcXQoiGkEZVCNFe+BHoW7C/X/z7Ay54gft65ukAEEL40MwWAtbATeBHAX8ys+VDCBOBabi2NKVzlXlM03UArgBOKTjvi+T/b/MHQwivmdnTwA7AU/HvVSGEn4tuGkKYbGajgJWAtYE9gFPNbK0QwjONyEso92BCCNEUpFEVQrQXXgeWMbOOuf3L4cLlW9VeKITwYwjhrhDCwcDiQA9KZvXPgDly91maYn6Z/RN9YBfCtargvrFLAG+HEMbnti+ryOblwFZmNjLe/7IGnmlqCOHfIYSjgWVwP9dtGpGXV3FBNyX/WwghGoUEVSFEe+FcYE7gkjiD/RdmthU+aeryEMIXlZM7Zrazme1qZiPjrPdtgd64oAbwAC64Hh/v8X/A7wouFXCt5apmtjguWH4LXBWPnwQsDFxpZsuZ2bxmtoaZnW1m81WR1atxDfKlwIshhBcqPNOvzWz/WC7DcKF7aPJM1eTlDGBLM9vXzOY3s51wP1khhGgyElSFEO2CEMJruAazHzAOeBE4Ap+lvnsjLvUlsBPwIK79/AOwWwjhX/E+bwC7AlsBLwO/BQ4vuM60uP88XGM5GNgwhPBtkt8VgV64b+mrwAVAd+CrKp73c+B2fAb+5ZXP5ktgNHAX8CYejeAE4OJq8xJCuAmfOHUwXrbbAoc0lE8hhKiEhSCXIiGEmJmY2RjgwhCC5gkIIUQFpFEVQgghhBCtklYpqJrZ3mb2dFz15dJa50cIIYQQQsx8WqXpP678Mg0PUN09hDCmtjkSQgghhBAzm1apUQ0h3BhCuJm68fkKMbMRZvaQmU0ys8/N7NqWz6EQQgghhGhpZgVH/uOBe/Dg213wdajrYWa7AbsB9OzZc5mFFlpopmVQCCGEEKI98swzz3weQpi9qelnBUF1Cr729lwhhA8os2RfCOF84HyAUaNGhaeffnrm5VAIIYQQoh1iZu/NSPpWafpvJAfjyw7+x8xeMbPf1jpDQgghhBBixmnzGtUQwid4cG3MbGXgPjP7dwhhfP5cMxsNjB4xYsRMzqUQQgghhGgsrVKjamadzKwb0BHoaGbdzKxQqDaz/zOzIfHnl/iyhFOLzg0hjAsh7Na3b98WybcQQgghhGg+WqWgChwJfA8cCmwX/z+yzLnLAk+a2WTgVmDfEMK7RSea2WgzO3/SpEktkGUhhBBCCNGctMo4qi1NuclU06ZN44MPPuDbb7+tQa7ErEjPnj0ZMmQIHTq01jGhEEII0XKY2TMhhMKITNXQ5n1Um5PPP/8cM2PBBReUYCFmmGnTpvHhhx/y+eefM8ccc9Q6O0IIIUSbo11JYw2Z/r/66isGDRokIVU0Cx06dGDQoEHI1UQIIYRoGu1KImtoMtXUqVPp3LnzTM6VmJXp3LkzP//8c62zIYQQQrRJ2pWgWg1mVussiFkI1SchhBCi6bQrQVWz/oUQQggh2g7tSlBVHNXWzUknncQuu+zSrNecMGECZibzuxBCCNEG0az/NoKZ8dZbb5GuqnXssccyfvx4rrzyyhrmrPk4/PDDa50FIYQQQrQiJKg2Ezc/9yGn3f0GH331PXP1685B6y3IJkvNXetstSqmTp1Kx44da50NIYQQQrQR2pXpv6V8VG9+7kMOu/ElPvzqewLw4Vffc9iNL3Hzcx82630q8eCDDzJkyBDOOOMM5phjDgYPHswll1wy/fgdd9zBIossQu/evZl77rk5/fTTAbj00ktZeeWV61zLzBg/fjwAY8aMYY899mCdddahd+/erLbaarz33nvTz3399ddZZ511GDBgAAsuuCDXXXfd9GNjxoxhzz33ZIMNNqBnz56cfPLJzDnnnEydWlrh9qabbmKJJZYAXEO83XbbAfDDDz+w3XbbMdtss9GvXz+WXXZZPv30UwAmTZrEzjvvzODBg5l77rk58sgjp19z6tSpHHjggQwcOJD55puP22+/vdnKWAghhBAzl3YlqLaUj+ppd7/B91Om1tn3/ZSpnHb3G816n4b45JNPmDRpEh9++CEXXXQRv/vd7/jyyy8B2HnnnTnvvPP45ptvePnll1lzzTWrvu7YsWM56qij+Pzzz1lyySXZdtttAfj2229ZZ5112Gabbfjss8+4+uqr2WuvvXjllVemp73qqqs44ogj+OabbzjwwAPp2bMn999/f53j22yzTb17XnbZZUyaNImJEyfyxRdfcO6559K9e3cAdtxxRzp16sT48eN57rnnuOeee7jwwgsBuOCCC7jtttt47rnnePrpp/nnP//Z+IIUQgghRKugXQmqLcVHX33fqP0tRefOnTn66KPp3LkzG2ywAb169eKNN96YfuzVV1/l66+/pn///iy99NJVX3fDDTdk1VVXpWvXrpx44ok8/vjjTJw4kdtuu4155pmHnXbaiU6dOrH00kuz2Wab1REOf/3rX7PSSivRoUMHunXrxtZbb83VV18NwDfffMMdd9zB1ltvXfgsX3zxBePHj6djx44ss8wy9OnTh08//ZQ777yTs846i549ezLHHHOw//77c8011wBw3XXXsd9++zF06FAGDBjAYYcdNiNFKoQQQogaIkG1GZirX/dG7W8KHTt2ZMqUKXX2TZkypc4CBbPNNhudOpXcjnv06MHkyZMBuOGGG7jjjjsYPnw4q622Go8//njV9x46dOj0/3v16sWAAQP46KOPeO+993jyySfp16/f9G3s2LF88sknhWkBttlmG2688UZ+/PFHbrzxRpZeemmGDx9e757bb7896623HltttRVzzTUXBx98MFOmTOG9995jypQpDB48ePo9d999dz777DMAPvroozr3LLq2EEIIIdoG7UpQbSkf1YPWW5DunetOEureuSMHrbdgs91j2LBhTJgwoc6+d999t2pBbNlll+WWW27hs88+Y5NNNmGLLbYAoGfPnnz33XfTz0uFzIyJEydO/3/y5Mn873//Y6655mLo0KGsttpqfPXVV9O3yZMn849//GP6+fmA94sssgjDhw/nzjvvLGv2B9eoHnPMMbz66qs89thj3HbbbVx++eUMHTqUrl278vnnn0+/59dffz3d3WDw4MF18vv+++9XVT5CCCGEaH20K0G1pXxUN1lqbk7edHHm7tcdA+bu152TN128WWf9b7nllpxwwgl88MEHTJs2jfvuu49x48ax+eabN5j2p59+YuzYsUyaNInOnTvTp0+f6bPvR44cySuvvMLzzz/PDz/8wLHHHlsv/R133MEjjzzCTz/9xFFHHcXyyy/P0KFD2WijjXjzzTe54oormDJlClOmTOGpp57itddeq5ifbbbZhnPOOYd///vf/N///V/hOQ888AAvvfQSU6dOpU+fPnTu3JmOHTsyePBg1l13XQ444AC+/vprpk2bxttvv81DDz0EwBZbbME555zDBx98wJdffskpp5zSYPkIIYQQonXSrgTVlmSTpebm0UPX5N1TNuTRQ9ds9tBURx99NCuuuCIrr7wy/fv35+CDD2bs2LEstthiVaW/4oormGeeeejTpw/nnnvu9NirCyywAEcffTRrr702888/f70IAOCC5XHHHceAAQN45plnGDt2LAC9e/fmnnvu4ZprrmGuueZizjnn5JBDDuHHH3+smJett96aBx98kDXXXJOBAwcWnvPJJ5+w+eab06dPHxZeeGFWW2216REBLr/8cn766ScWWWQR+vfvz+abb87HH38MwK677sp6663HyJEjWXrppdl0002rKh8hhBBCtD4shFDrPMx0Ro0aFZ5++ul6+1977TUWXnjhGuSo9TJmzBiGDBnCCSecUOustFlUr4QQQrRXzOyZEMKopqaXRlUIIYQQQrRKJKgKIYQQQohWSbtaQtXMRgOjR4wYUeustBkuvfTSWmdBCCGEEO2UdqVRbalZ/0IIIYQQovlpV4KqEEIIIYRoO0hQFUIIIYQQrRIJqkIIIYQQolUiQVUIIYQQQrRK2tWs/7bMoosuyt/+9jdWX331Jl/j0ksv5cILL+SRRx5pdNoJEyYw77zzMmXKFDp1arlqM3bsWC677DLuueeeFruHEEIIMSsxz6G3V33uhFM2bMGcND8SVJuRY489ts7f5uSVV15p9mvWmiLhd9ttt2Xbbbetcc7qY2a89dZbKLSZEEKIlqAxwia0PYGzqbQr07+ZjTaz8ydNmlTrrAghhBBCiAZoV4JqS8VRHTNmDL169eKkk07ipJNOolevXowZM6ZZ7zHPPPNw3333Aa6x3WKLLdhhhx3o3bs3iy66KE8//fT0cydOnMimm27K7LPPzmyzzcbee+9d73oTJkzAzPj555+n71t99dW58MILAZg6dSoHHnggAwcOZL755uP22+uO9CZNmsTOO+/M4MGDmXvuuTnyyCOZOnUqAOPHj2e11Vajb9++DBw4kC233LLwmVZddVUA+vXrR69evXj88ce59NJLWXnllaefY2b8/e9/Z/7556d3794cddRRvP3226ywwgr06dOHLbbYgp9++mn6+bfddhtLLrkk/fr1Y8UVV+TFF1+cfuy1115j9dVXp1+/fiy66KLceuuthc8O1MlHls+RI0fSq1cvrr32Wj7//HM22mgj+vXrx4ABA1hllVWYNm1a4XMKIYQQomm0K0G1pbj00kuZPHkyhx9+OIcffjiTJ09u8RWdbr31Vrbaaiu++uorNt544+nC6NSpU9loo40YPnw4EyZM4MMPP2SrrbZq9PUvuOACbrvtNp577jmefvpp/vnPf9Y5vuOOO9KpUyfGjx/Pc889xz333DNd0DvqqKNYd911+fLLL/nggw/YZ599Cu/x73//G4CvvvqKyZMns8IKKxSed9ddd/HMM8/wxBNPcOqpp7LbbrsxduxYJk6cyMsvv8zVV18NwLPPPstvf/tbzjvvPL744gt23313Nt54Y3788UemTJnC6NGjWXfddfnss8/4y1/+wrbbbssbb7zRYFlk+XzhhReYPHkyW265JWeccQZDhgzhv//9L59++iknnXQSZlZd4QohhBCiKiSotlFWXnllNthgAzp27Mj222/PCy+8AMB//vMfPvroI0477TR69uxJt27d6mgoq+W6665jv/32Y+jQoQwYMIDDDjts+rFPP/2UO++8k7POOouePXsyxxxzsP/++3PNNdcA0LlzZ9577z0++uijJt8/5ZBDDqFPnz4suuiiLLbYYqy77rrMN9989O3bl1/96lc899xzgAvXu+++O8svvzwdO3Zkxx13pGvXrjzxxBM88cQTTJ48mUMPPZQuXbqw5pprstFGG00XchtL586d+fjjj3nvvffo3Lkzq6yyigRVIYQQopmRoNpGmXPOOaf/36NHD3744Qd+/vlnJk6cyPDhw2d4Zv5HH33E0KFDp/8ePnz49P/fe+89pkyZwuDBg+nXrx/9+vVj991357PPPgPg1FNPJYTAcsstx6KLLsrFF188Q3kZNGjQ9P+7d+9e7/fkyZOn5+uMM86Ynqd+/foxceJEPvroo+nP06FDqcoPHz6cDz/8sEl5OuiggxgxYsR0ofmUU05p4tMJIYQQohya9T+LMXToUN5//31+/vnnisJqz549Afjuu+/o06cPAJ988sn044MHD2bixInTf7///vt17tG1a1c+//zzwnvMOeecXHDBBQA88sgjrL322qy66qr1Zsw3twZy6NChHHHEERxxxBH1jj388MNMnDiRadOmTRdW33//fRZYYAHAy+O7776bfn5aFkX07t2bM844gzPOOINXXnmFNdZYg2WXXZa11lqrGZ9ICCFEW2NWDhVVC6RRncVYbrnlGDx4MIceeijffvstP/zwA48++mi982affXbmnnturrzySqZOncrFF1/M22+/Pf34FltswTnnnMMHH3zAl19+WUdjOHjwYNZdd10OOOAAvv76a6ZNm8bbb7/NQw89BMD111/PBx98AED//v0xMzp27FiYhw4dOvDOO+80y7PvuuuunHvuuTz55JOEEPj222+5/fbb+eabb1h++eXp2bMnp556KlOmTOHBBx9k3Lhx0/13l1xySW688Ua+++47xo8fz0UXXVTn2oMGDaqTz9tuu43x48cTQqBPnz507Nix8BmFEEII0XQkqM5idOzYkXHjxjF+/HiGDRvGkCFDuPbaawvPveCCCzjttNOYbbbZeOWVV1hxxRWnH9t1111Zb731GDlyJEsvvTSbbrppnbSXX345P/30E4sssgj9+/dn88035+OPPwbgqaeeYvnll6dXr15svPHGnH322cw777z17t+jRw+OOOIIVlppJfr168cTTzwxQ88+atQoLrjgAvbee2/69+/PiBEjpk9q69KlC7feeit33nknAwcOZK+99uLyyy9noYUWAmD//fenS5cuDBo0iB133LFeLNdjjz2WHXfckX79+nHdddfx1ltvsfbaa9OrVy9WWGEF9tprrxlajEEIIYQQ9bEQQq3zMNMZNWpUSMM5Zbz22mssvPDCTb7uSSedBMDhhx/e5GuIWY8ZrVdCCCFmLjMSfL+ppv9a3HNmYGbPhBBGNTW9fFSbEQmoQgghhBDNhwRVIYQQQsxyaEnSWQP5qAohhBBCiFaJBFUhhBBCCNEqkaCaoz1OLhMth+qTEEII0XQkqCZ069aNL774QsKFaBZCCHzxxRd069at1lkRQggh2iSaTJUwZMgQPvjgA/773//WOitiFqFbt24MGTKk1tkQQggh2iQSVBM6d+5cGJheCCGEEELMfNqV6d/MRpvZ+ZMmTap1VoQQQgghRAO0K0E1hDAuhLBb3759a50VIYQQQgjRAO1KUJVGVQghhBCi7dCuBFVpVIUQQggh2g7tSlAVQgghhBBth3YlqMr0L4QQQgjRdmhXgqpM/0IIIYQQbYd2JagKIYQQQoi2Q7sSVGX6F0IIIYRoO1QlqJrZLWX239i82WlZZPoXQgghhGg7VKtRXaPM/tWbKR9CCCGEEELUoVOlg2b2x/hvl+T/jPmA91okV0IIIYQQot1TUVAFhsa/HZL/AQIwETi2BfLUYpjZaGD0iBEjap0VIYQQQlTBPIfe3qjzJ5yyYQvlRNSCioJqCGEnADN7LIRwwczJUssRQhgHjBs1atSutc6LEEIIIYSoTEMaVQBCCBeYWV9gQaBX7tj9LZExIYQQQgjRvqlKUDWzMcDfgMnAd8mhgPuqCiGEEEII0axUJagCJwKbhxDubMnMtDTyURVCCCGEaDtUG56qE3BPS2ZkZqA4qkIIIYQQbYdqBdU/AUeaWbtayUoIIYQQQtSOak3/+wNzAgeb2RfpgRDCsGbPlRBCCCFmGRRiSjSVagXV7Vo0F0IIIYQQQuSoNjzVQy2dESGEEEIIIVLKCqpmdkQI4cT4f3751OmEEI5uiYy1BJr1L4QQQgjRdqg0OWpI8v/QClubQbP+hRBCCCHaDmU1qiGEPZP/d5o52RFCCCGEEMKpdjIVZjY/sDUwN/AhcHUI4a2WypgQQgghhGjfVBUXNfp2PgMsBPwPWBB42sw2bsG8CSGEEEKIdky1GtWTgF+HEB7IdpjZ6sBfgVubP1tCCCGEEKK9U+1KU0OAh3P7HqHuhCshhBBCCCGajWoF1eeBA3L7/hD3CyGEEEII0exUa/rfExhnZvsCE4FhwGSgTfmoKo6qEEII0XQasxSqlkEVzUG1K1O9bmYLA78E5gI+Ap4MIUxpycw1NyGEccC4UaNG7VrrvAghhBBCiMpUa/oHCLltWovkSAghhBBCCKrUqJrZEsDNQFc8huoQ4Acz+00I4YWWy54QQgghhGivVKtRvRj4GzAkhLAcHvT/r3G/EEIIIYQQzU61guoCwFkhhAAQ/54NzN9SGRNCCCGEEO2bagXVO6g/w380UP30PyGEEEIIIRpBteGpOgLXmNkzeHiqocAywC1mdnl2Ughhh+bPohBCCCGEaI9UK6i+HLeMV4G7mz87QgghhBBCONXGUT2upTMihBBCiOppavD9xqTLpxViZtOYOKpCCCGEEELMNCSoCiGEEEKIVokEVSGEEEII0SqRoCqEEEIIIVol1c76x8wGAcsBAwHL9ocQtDqVEEIIIYRodqoSVM1sE+BK4C1gUeAVYDHgEbSMqhBCCCGEaAGqNf2fAOwUQlgK+Db+3Q14psVyJoQQQggh2jXVCqrDQgjX5/ZdBrSplajMbLSZnT9p0qRaZ0UIIYQQQjRAtYLqZ9FHFWCCma0A/AJfWrXNEEIYF0LYrW/fvrXOihBCCCGEaIBqJ1NdAKwM3AD8GXgAmAac2UL5ahHMbDQwesSIEbXOihBCCKFVooRogKo0qiGEP4UQboj/Xw4sACwTQjiyJTPX3EijKoQQQgjRdqhKUDWzW9LfIYT3QwivmdmNLZMtIYQQQgjR3qnWR3WNMvtXb6Z8zBQ0mUoIIYQQou1Q0UfVzP4Y/+2S/J8xH/Bei+SqhQghjAPGjRo1atda50UIIYQQQlSmoclUQ+PfDsn/AAGYCBzbAnkSQgghZjozMrGpMWk1IUqI6qkoqIYQdgIws8dCCBfMnCy1HJr1L4QQsz4SGoWYdagqPFUmpJpZb2AgYMmxd1oma82PTP9CCNE2UNgmIQRUKaia2cLAVcBI3Oxv8S+0saD/QgghhBCibVBtwP9/4EH+1wDeBeYBTgYea5lstQwy/QshRONpqildWlEhxIxSraA6ElgnhDDFzCyEMMnMDgJeBq5suew1LzL9CyHaKxIahRBtkWrjqP4AdI7/f25mw2La2VokV0IIIYQQot1TraD6MLBF/P+fwJ3AQ8D9LZEpIYQQQgghqp31v0Xy83DgFaAXcHlLZEoIIYQQQohqfVSnE0KYBlzRAnlpcTSZSgjR1lGMUCFEe6KsoGpmV1AKQVWWEMIOzZqjFkSTqYQQQggh2g6VNKrjk/8HAjsC44D3gGHAaOCylsuaEELMmmgGvhBCVEdZQTWEcFz2v5ndDWwYQng42bcycFTLZk8IIVoWre8uhBCtl2p9VH8JPJHb9ySwQvNmRwghmoaERiGEmPWoVlB9DjjJzI4OIXxvZt2B44DnWyxnQoh2h0ziQgghUqqNozoGWAmYZGafApOAlYE2M5EKfNa/mZ0/adKkWmdFCCGEEEI0QLVxVCcAK5rZUGAu4OMQwvstmbGWQLP+hWh5pBUVQgjRXDQqjmoIYSIwsYXyIoQQQgghxHQaHfBfCNE+kGZUCCFErZGgKsRMohZhkCRsCiGEaMs0OJnKzDqY2Zpm1mVmZEgIIYQQQgioQqMaQphmZreEEHrPjAwJMTNQkHchhBCi9VNteKp/m9kvWzQnQgghhBBCJFTro/oecKeZ3YLP+g/ZgRDC0S2RsZbAzEYDo0eMGFHrrAghhBBCiAaoVqPaHbgZF1CHAEOTrc0QQhgXQtitb9++tc6KEEIIIYRogGoD/u/U0hkRoinIX1QIIYSYdak6PJWZLQxsDgwKIextZgsCXUMIL7ZY7oQQQgghRLulKtO/mf0f8G9gbmCHuLs3cGYL5UsIIYQQQrRzqvVR/SOwTghhD2Bq3PcCMLJFciWEEEIIIdo91Zr+58AFUyjN+A/J/0I0Ga2eJIQQQogiqhVUnwG2By5P9m0F/KfZcyRqigLhCyGEEKK1UK2g+nvgHjPbGehpZncDCwDrtljOhBBCCCFEu6ba8FSvm9lCwEbAbXjQ/9tCCJNbMnNCCCGEEKL9UnV4qhDCd2b2KPAu8JGE1NaLfD6FEEIIMStQbXiqYWb2MDABuB2YYGaPmNnwKtPvbWZPm9mPZnZpk3MrhBBCCCHaDdWGp7oMn1DVL4QwB9AfeCrur4aPgBOAixudQyGEEEII0S6p1vS/DLBuCGEKQAhhspkdAnxRTeIQwo0AZjYKGFLpXDMbA+yKRxTYCfgfsB0+eet4oCtwUAjhsnj+BsDpwFDga+DPIYTTq3yuVo1M+EIIIYRoz1SrUX0CWC63bxTwePNmZzrLAy8CswFXAdcAywIjcKH1r2bWK557EbB7CKE3sBhwf9EFzWy36H7w9H//+98Wynb1vP3227XOghBCCCFEq6ZajerbwB1mdjs+438osAFwlZn9MTsphHB0M+Xr3RDCJQBmdi1wBPDHEMKPeJisn3Ch9XlgCrCImb0QQvgS+LLogiGE84HzAUaNGlXzhQoGDRpU6ywIIYQQQrRqqhVUuwE3xv/nAH4EbgK640IrNO8qVZ8m/38PEELI78s0qpsBRwKnmNmLwKEhhEJNr5mNBkaPGDGiGbNaGZnvhRBCCCGaRrVxVHdq6Yw0lRDCU8CvzawzsDdwHSXhOX/uOGDcqFGjdp2JWRRCCCGEEE2gWh/VGcLMOplZN6Aj0NHMuplZ1TFcK1y3i5lta2Z940Svr4GpM3pdIYQQQghRe2aKoIqb5r8HDsUnQ30f9zUH2+NxXb8G9ojXL8TMRpvZ+ZMmTWqmWwshhBBCiJZihrWa1RBCOBY4tspzLwUuTX6PByx3Thriav1G5EOmfyGEEEKINsLM0qgKIYQQQgjRKKoWVM1sHTO7yMzGxd+jzGzNlsta8yPTvxBCCCFE26EqQdXM9gH+AbwFrBp3f48vi9pmCCGMCyHs1rdv31pnRQghhBBCNEC1GtX9gLVDCKcA0+K+14EFWyJTQgghhBBCVCuo9sZXpIJSYP/OwE/NnqMWRKZ/IYQQQoi2Q7WC6r/x0FIpvwceaN7stCwy/QshhBBCtB2qDU+1DzDOzHYFepvZG3hw/dEtljMhhBBCCNGuqXYJ1Y/NbFlgOWAY7gbwnxDCtMopWxdmNhoYPWLEiFpnRQghhBBCNEDV4amC82QI4foQwhNtTUgFmf6FEEIIIdoS1YanGmlm95vZ/8zsp7hNMbM2NZlKCCGEEEK0Har1Ub0auAGfQPV9y2VHCCGEEEIIp1pBdU7g6BBCaPBMIYQQQgghmoFqfVQvA7ZpyYzMDBRHVQghhBCi7VCtoHoKcLyZvRJ9VadvLZm55kaTqYQQQggh2g7Vmv7/CbwL3IR8VIUQQgghxEygWkF1SWC2EIJm+QshhBBCiJlCtab/h4FFWjIjQgghhBBCpFSrUX0XuMfMbgI+TQ+EEI5u9lwJIYQQQoh2T7WCag/gdqALMLTlstOyaAlVIYQQQoi2Q1WCaghhp5bOyMwghDAOGDdq1Khda50XIYQQQghRmbKCqpnNE0KYEP+fr9x5IYR3WiBfQgghhBCinVNJo/oS0Dv+Px4IgOXOCUDHFsiXEEIIIYRo55QVVEMIvZP/q40OIIQQQgghRLNQlQBqZueU2X9Ws+ZGCCGEEEKISLWa0jFl9m/fTPkQQgghhBCiDhVn/ZvZb7Pzkv8z5gM+b5FcCSGEEEKIdk9D4akyjWkX6mpPAx74f8eWyFRLoTiqQgghhBBth4qCaghhDQAzOyGEcOTMyVLLoTiqQgghhBBth2oD/h8JYGZzAL1yxxRHVQghhBBCNDtVCapmth5wMTA4d0hxVIUQQgghRItQ7az/vwPHAz1DCB2STUKqEEIIIYRoEarSqAL9gfNCCKElMyOEEEIIIURGtRrVi4CdWjIjQgghhBBCpFSrUf0l8HszOxT4JD0QQli12XMlhBBCCCHaPdUKqhfGTQghhBBCiJlCteGpLmvpjAghhBBCCJFSbXiq/PKp0wkhXNx82RFCCCGEEMKp1vS/fe73nMAvgEfx+KpCCCGEEEI0K9Wa/tfI74ta1oWbPUdCCCGEEEJQfXiqIi4Fdm6mfAghhBBCCFGHan1U8wJtD2A74KvmzpAQQgghhBBQvY/qz0B+VaoPgV2bNztCCCGEEEI41Qqq8+Z+fxtC+Ly5M9PSmNloYPSIESNqnRUhhBBCCNEADfqomllH4H7gkxDCe3Frc0IqQAhhXAhht759+9Y6K0IIIYQQogEaFFRDCFOBqUC3ls+OEEIIIYQQTrWz/s8CrjOz1czsF2Y2X7a1YN6aHTMbbWbnT5o0qdZZEUIIIYQQDVCtj+pf4991cvsD0LH5stOyhBDGAeNGjRqlSWBCCCGEEK2cagP+z0i8VSGEEEIIIRpNuxJAZfoXQgghhGg7tCtBVbP+hRBCCCHaDu1KUBVCCCGEEG2HdiWoyvQvhBBCCNF2aFeCqkz/QgghhBBthxkSVM3s9ubKiBBCCCGEECkzqlF9pFlyMZOQ6V8IIYQQou0wQ4JqCOHk5srIzECmfyGEEEKItkNVAf8rLJX6I/BxCGFa82VJCCGEEEKI6pdQHY8vlwpgyf8A08zsVmCvEMKnzZk5IYQQQgjRfqnW9L8rMBZYAOgGLAhcCewFLI4LvH9riQwKIYQQQoj2SbUa1eOAESGEH+Lv8Wa2J/BmCOE8MxsDvNUSGWxOzGw0MHrEiBG1zooQQgghhGiAajWqHYB5cvuGAR3j/5OpXuitGZpMJYQQQgjRdqhWuDwLuN/MLgEmAkOAneJ+gA2Bx5s7c0IIIYQQov1SlaAaQjjVzF4E/g9YGvgY2DmEcFc8fjNwcwvlUQghhBBCtEOqDU81MAqld7VwfoQQQgghhACq91F938zuMLNtzaxHi+ZICCGEEEIIqhdUhwG3AXsCn5rZ1XE50lY/gSpFS6gKIYQQQrQdqhJUQwifhxD+HkJYGVgUeAE4EfdVbTNo1r8QQgghRNuhWo1qyqC4DQS+atbcCCGEEEIIEalKUDWzRczseDN7m9Ls/k1CCPO3WM6EEEIIIUS7plof00eBG4DdgPtDCAHAzDqEEKa1VOaEEEIIIUT7pVpBdVAI4afsh5ktDuwIbAPM1RIZE0IIIYQQ7ZtqJ1P9ZGazm9m+ZvYs8DwwCti3JTMnhBBCCCHaLxU1qmbWGdgYGAOsB4wHrgaGA1uEED5r6QwKIYQQQoj2SUMa1U+B84A3gF+GEBYJIRwP/FQ5WetEcVSFEEIIIdoODQmqLwL9gOWBZc2sf4vnqAVRHFUhhBBCiLZDRUE1hLA68AvgHuBA4BMzGwf0BDq3eO6EEEIIIUS7pcHJVCGE90IIx8eYqWvhq1FNA14ws1NbOoNCCCGEEKJ90qiVqUIIj4QQdgPmBPYBFm+RXAkhhBBCiHZPU5ZQJYTwQwjh6hDCr5o7Q0IIIYQQQkATBVUhhBBCCCFaGgmqQgghhBCiVSJBVQghhBBCtEokqAohhBBCiFaJBFUhhBBCCNEqkaAqhBBCCCFaJRJUhRBCCCFEq0SCqhBCCCGEaJVIUBVCCCGEEK0SCapCCCGEEKJV0q4EVTMbbWbnT5o0qdZZEUIIIYQQDdCuBNUQwrgQwm59+/atdVaEEEIIIUQDtCtBVQghhBBCtB3alaAq078QQgghRNuhXQmqMv0LIYQQQrQd2pWgKo2qEEIIIUTboV0JqtKoCiGEEEK0HdqVoCqEEEIIIdoOElSFEEIIIUSrpF0JqvJRFUIIIYRoO7QrQVU+qkIIIYQQbYd2JagKIYQQQoi2Q7sSVGX6F0IIIYRoO7QrQVWmfyGEEEKItkO7ElSFEEIIIUTbQYKqEEIIIYRolUhQFUIIIYQQrZJ2JahqMpUQQgghRNuhXQmqmkwlhBBCCNF2aFeCqhBCCCGEaDtIUBVCCCGEEK0SCapCCCGEEKJVIkFVCCGEEEK0StqVoKpZ/0IIIYQQbYd2Jahq1r8QQgghRNuhXQmqQgghhBCi7SBBVQghhBBCtEokqAohhBBCiFaJBFUhhBBCCNEqkaAqhBBCCCFaJRJUhRBCCCFEq6RdCaqKoyqEEEII0XZoV4Kq4qgKIYQQQrQd2pWgKoQQQggh2g4SVIUQQgghRKtEgqoQQgghhGiVSFAVQgghhBCtEgmqQgghhBCiVSJBVQghhBBCtEokqAohhBBCiFaJBFUhhBBCCNEqkaAqhBBCCCFaJTURVM1sbzN72sx+NLNLa5EHIYQQQgjRuulUo/t+BJwArAd0r1EehBBCCCFEK6YmGtUQwo0hhJuBLxo618yONbMrk9/zmFkws07x9xgze8fMvjGzd81s25bLuRBCCCGEmFlYCKF2Nzc7ARgSQhhT4ZxjgREhhO3i73mAd4HOQFfgY2DZEMIbZjYYGBBCeKXgOrsBu8WfCwJvNN+TCCGEEEKIAoaHEGZvauJamf6bk2nAYmb2fgjhY1xwrUcI4Xzg/JmaMyGEEEII0WTa9Kz/EMK3wJbAHsDHZna7mS1U42wJIYQQQohmoK0Iqt2S//ulB0IId4cQ1gEGA68DF8zEfAkhhBBCiBaiVuGpOplZN6Aj0NHMumWTo8qwupmNMLOuwD5x3wAzG2RmG5tZT+BHYDIwtWVzL4QQQgghZga10qgeCXwPHApsF/8/ssL5LwBXAxPxiVSPA9fi+T8AD3f1P2A1YK8Wy7UQQkTMrEP8262hc1vo/laL+zaFGclrczxnWyqrppDVRVGeBpRhldLN1LKd0bo6K9b1WoWnOjaEYLnt2ApJPg4hLBtCmCOEcEIIYcUQwhohhI9DCKuFEPqGEPqFEFYPIbw6s55jZtKUypf/wBpzjaZWdjPrGzXcM5VZ8ePMY2aDanz/5RrzbtN30tS6Z2ZzNVUQbMk6YWYWQphmZnMC1zW1zpvZMk3pCM1sRJjJIVvMbHcz2ya3r6oyzvJqZpvHNqLqd5Ok3drMBlSTNleHeja2rGZUODGztc1syIxcoxH3yupibzNbsYnXGDYD918sWjtnGmY2rLHfdwjhZzPrYWabNDLdtHjPpRuTLsXMqo4Xn9T3tasp14Jy6NCY+7UFNAprYcysY/zbJbovzGVmQ6ppCM1sBzMbCaXK29jbx+v0z67R2I6lCVwErNXYRGZ2spkdl9vXqM5sBoRrS/+2JGa2oJkt2IR0i+ICUZM6BHMarVEws5FmNq+ZLQH8A4+yURVJg9sp+b+aMp4zEcqfBFZvRH6nP2Nj6rCZLdzEjuhiYGKc2NkozOwsYLesI4z7qmkXlgLeNLMejb1n7jqNGTx0B/4EvJXur6aMzexXZraXmQ0FrgF6VftuzGwTMzvczGYHxgL9q0zbJ/lWnjGzNaq5X0YinPzBPCRiNXldy8x2iT/vwedNzAy2j4O5c4E1Y17KvtukvRsW3wnAWDObt9obmtlCZjZ3LJsL8FCR1aYdFP+uZmYHV3F+lt8hcaDSE4/gM7AR97wwDir/CuyeXreBdNm5v6eR81/MbLb4dy9g4yrTrGFmS5jZSsAZwJSG0iRt66px1/HA0Y3Ja2tnVghP1aoJIWQ+s1cDQ4D5gTuBR8zsuhBC4aIHUTDYCXjHzB4F7g4hfBiPWUONtZmtDextZgsAr5jZa8DFIYQJDaRbCfgV7koxNoTwaZWPipltCawQQti82jQxXX9gb2CVdH8jOrMTgauy+Llm1iHt/BtI2wnoCUxKPviq0+euVTFdHLT8FfivmV0DPNmI8r0YuCOE8GN2raRuNZSvrYCRwB3Aw1XeLyubfYBv8DrxYAjh+yrTjgB+i9f3CWb2InB1COHnKpJvBZxhZg8An4cQ7sryU5Q+K3cz6xNC+DoKnHsAu1cpSBne8d1kZh+FED5p4PzsfqsDSwG/SY79H/B0COHdBq4xJ14+y6X7q6x3fwGOCyF818h60DGEMNXMFgc2xDUvbwPjQgjfNZD8XOCeEMJT8VoGbATcWcU77YYPXo+M95rejkGD3/n/8BUM/xDv9XZM2yGmLVdeuwCHmNldeLzwB2K6BssrvsN5gPHAISGEMxt4voxOwNmxPfpXUladgKmVnjNr02OZ9AshfFnNDc1seWBVYF1gU2A4lAbuRfdM9o0BepvZKGByVmcb6l9i2f8BfzfrA8+FEL6uMr9z4e/lAXzgu3PcX/a9JHk5GPgErw9fhBD+W+U9e8W8PgHMDiyaXbfSfWO635vZn+KuX8b9XYCfG2jr1wY2NbPb8DZ/gSry2R3/LrsAmwOXNaIfWxLYz9zisQMwV9zfYN1rE4QQtLXQBnSIf3cGXgP644sNHAc8COzbQPqVgNOBK/HOaW2ge3LcyqT7HfAAPgL8HXAhPsK/B9ggzVsu3bLAe8B1wH9xf+C5GvG87wC7NJS/gnTXAxckv3sAZ6bPWiHtL4DbgEfwUWTvfPmXSTcfLsy8E9/FLcAWVea3U/w7F7AEsEb6zJWeGxcYLwAeBU7FO5neDdxvC+CN3L79gfmryOtieIe7HTBHE+pwZ+ByXFg9Dhc4Zm8gzYbAfcC/Y929K76fa4Clq6kbwOK47/o3wP+l77RM3e0DPIQP7p4BDsrynzuvKO3f8UFAum8QsDDQsUK68cBhye+VgK+BblWU6zjg7OT3bLgvfp8qyvbrtC5WqufxnAG57+INfLA8Dv/WLwF+1UAd+iG37zzg71U8Z7aozBjgK+DemHbRSu8k7s/Kfj3gS+Bl4FZg8fw5ZdJvEuvQy8CSaZpy9S8e2xo4C/gcuLKh+pO9h/h33lhnp+Dt2mz5c8qkz/qKQ/A2e/Zq7huPLYzP03gUn7Oxdvp8Rc+Kt1ML4/3Ld8CfgRWBrpXS5errFfgE5iNj2n5V1IfFgIOAx/A5J0um77CBey6Ba9W/xYXWRaiiP0yOPw28iPfFfyiqa2XS3R3v+Tzwy9y3V64eDY916GN8Tk1voEcV5dMlluvnuAVjW5J2u8L9BgIr4N/2q/hAbUg19actbDXPwKy+xYbvFGDb3P5tgM+AecukyxquAcClsUF4FvgjrrUsd79+eKO+GqVOoktsvG4F7i/3wcTr7x//7w78J+ZzLVzAXh4fGRY1fIfgZuFjce1btQ3eknikhhHJvkuAKxpRxnPED/NWXEDfIXe8KL/34R3J4cBRuPD4ZizruSvcK+0AXsM1lE/iHfCq+fdX4Tob44LCg7jQuRRlOjJ80HBk8vvXwJdVls2jwNEVjnet4hrvAofFd3slcCCuXSgUyPDBzo64iRdcoN8hlvnYhr6X+PdKXIhaFdeivIKvQJed1ymXbh5gXzxE3SRg9eRY5wr3mxP4gLoCxQF45zINF7DnLJPuIVxYzYTiB4CD0+coc89FcOFgJ9yUTax311TxvbyDd5o358qjkvD1AN5pzhfrzn1x/wBcC/dX4EZcqzy8IP3usSyOj7974BqqodXU9XjOC7gWbi58gPZgrE8VBfOY9jnioD6mfQc4h/LtWNbunQ/8C28b3sQHSkPL1aHcNU7Chb9z8YHMWkX3KEj3JHBU/P+fuMB6QqW0lNr6RfCBzsLxdy+8He9Z5l5Zuv8D3sZdZE7GNZUHkgj0FZ7zUfw7OwQfQBxMItQ3kPaN+D5OAS7DrWJl27Ek3WA8Ss9FuIB8MLBEleX7DC7I/SW+3x2pbsC+DrHNxLXPD+P92/r58sylWzTW/a745O+fgJtIhPL881IatCyN98U3xDRjgLmL7pNL/zGwZ9zOi3VxfaBLA/V9XVxO2Ao4G1cwbJfmr1y5tvat5hmY1Tdchf8l8ECyL6tYjwLrlUmXNUI34SOr9XFh4WZcg7gHsEhBumuAC+P/nXOVdBDe6e9bkG5r4KfcvrdxoeM+vNF+JH4IvXPnDcCFg0Ni43EDLvyNyp1XJDCujwtiN+AC8Yr4aDITcip1+KvjpunhuHlxFHAE3gneAKxSJt3JeOedCtNz4o3eM+RG22WucQQuGA+M9z0d99+7CBhWcP68+Ah8rtz+XWJeLsSX+B2QOz473jC/i3eanfGOe5t4vFJnuxSuTepRVP7A0NiQVRKMFgX+lvzeCBfqL8BN14vmzj8VuCupf6lgvzLeEe9S7n7ZM8X6MzDZdzTeSdxIGc0N3rE/gn8fD+ADnlRzdxM54QbvTB7A3XJ64gOIKfj3tWCsD+eVuV9/XNh8Dv8+vsw9Qznt24KxnlyLd/L746bM7HihYI37nj0S83o+LmCfR4VOM+7bOKZ7FhcOLyARfvDvZw9c2Ohf5t5r4Nqo/wHvEwc/+CC4oY63P7B1rr1YB3dnuRv30y2Xthuwdm7fYng79zplvtWY7hKiVgkfyPwN/46Or1Tn4/mv4BrZRWJdvBI4kShElkljBXldGhfSPwa2auCetyXlugbuqvMU3k+UtYbgbWBmqRiCt2Nn48L8TiSatVy6+YGb4/99ge1xwfEvwK4UDFqStEsQtc3xubfCB1t/x5UbZYXHWP5/j/+PwQdR5+CWx0pKgiWBe+P/veNzjwVOi3V8cIW0C1FXE98N11y/ivcVC1Qooy2T371wwX4K8McG3ufdwB7x/9/iyoxLcIGy3He2FKWBTmdcwXQ8Pvg4hESjW5D2GGDjpN7tiX9j/yCx+rXFreYZmNU3YJlYwV7HO8SNcDPlBrhwV1ajhXdon1LXbDc/ru35PKvQybERwA+49ioVUDtSEnzHUtC441qkl3CBaQTekbxD1PjiI8EBJBqJJO3FwCXx/8G4ZuufeOP+O8pojZP0fSl1Il8QBQMqm7uWwYWI/Ug0XrhGeR28oX4yPk+X5PgcsdznjL+75a57ID6wGFRwzw7J/2sSTdJ4Q90PF5xvxk1EaflvgJsD34hl9Uk871lcaH4VH7X/SKLZy5XPunhD9xV1BSKjjDAPDMP9subJzs2lG443ugs28H7yAm5PvCO7MpZzpkEYgJtat8mdn5bFzTTg8pJLmw4meuECyjTgF2n9Tt79nLg14Dd4x/koLgxeiftY5q/fHbg9nnMFrmlJtdfb4h1ZN0oDzLVxTVfX+Hs4bv58F9fSL1HmWdLy7whshms93sDdcobmyzo5vzeuSV0sedb1cGFmAnBAFWW5D24F+BYfmM6WO559E5W+u13xtuxV6ppBGxL86mkRce3qdrgbQq8q8p+/xoa4INevzPl90voR/18ed/P5gIK2LJ4zEPh18nsQMBofhF2Bm64ruiWRG6jE5/yZMoIYLpScg7edI3AN8KExv9cDy1dRPun9Fo7XupREa1iQpn/u9/yxnvw1boXa3OwZC8ptP7yPOZ3yA64lqPtdLxTz+jd8AlElN5S8kmQBXJC7Erc2FmodK1xvTly4PqmKczsn/y8V39G9Fc7PD4p74QPu+3HhfESZdB1yv/vjK3Bmg4i+lep77n6rxHp0A1FobotbzTPQHja8k/tl/Ag/wLUS1xJNd5Q3ofbGtYNr5PavhHf4C+b2j8QnbV0VG9XVKQmoWSd7EfUF3Hnx0epG+OjrMdwcv3vuvCKN6GIxL90L9p+Ea7HOxRvq/Ad4LnXN5UvE/L+Od/wL5BvD5NzngP2S3/lr98EF9r1y+88APiTpMHChLRO2FsGFlSKBPCvDHXAB40VgxTQPuEA/fy7dnrjG7Zv4/yK44LkV3ultjmu6Rpd51uwddsK1Ds/jQvjGDdS7wfG+x1C3s84ErFVwjWsljWqhmTL+Px+wVPJ7hVhfL8AF/gWTY53j37Mpo4kol4/0/cTfCxWUzez4oGRUkmYIrsm4Gdd+981dN3vna8TjV+Nm1FSgvJq6GuXu+IDhHtzUmT7jMrhWciL+nZXTqA5PyqMbJWE4G9jV0+7gnfn6+XLCB14742bMd4GVC9KmZdcb10C9hw+alqOMkIgPSNbFtV5HEL+ZeI2/44Pie6ngs4x/E5XqVxdyVoTGbMk7tPRvA2k64EJuNeemZT0C//bPoQHzdpKmrEWo4NyNcE3dvSTuAnh7NSrNT/Lc+XYv/72uXE1ey6QbndvXgTLlnCunJYrqYdF7yP1eCR+4z1dF/vK/1wJ+MwP1KGtHBlLB15acj3yFbycbPFvc0jQL4u1Ev4bqSEH9+2XR8zdQNoNwl595G0rbWreaZ2BW25IK2hcXBJbOGvLYwG+Ajzj/hQsn/cpcJ/tw/o6b2nZNjp2Ez+AvStcDN/dcjQvD032OcA3G1yTmZ1yYeRRvuLvhnei2uIB5P+5XVknD8ijRlFt0XmxAriGnRcMF2Ytwk+Sfqev4vSWuKXmFZCJNcnx9cpOLcscXzBrKXAPRMZbNJbhgcCQ5gTQ+/3PktMDJ+1gf17heg2uzbsI78XK+xlnDPhIfRX+Au3L0baAezR4bl7vju7yUKKDF93giLpjcR2Wt/K6xLHcjalbj/m6xfH+XO38VClwX8s9EeaFyQVxrcCsuCO1ANMnhQsnnRDNlLl01vrIdyDXqSfmOAy5Pnm0YdYXoIpP4bHGrpzXCBwXrx/zmNSM9cAvEw/igcKvcM24OnFJwzdGxzr+PCx9jcUG6Cz4p8I+4kHJ8Lt28uLC4aPrMuXo9Ah+cLlmm7DqSCMDxPd2CDwpPI9fh4u3Xbfhg7Alc+z+VZAIVLjw/QX3/+6qFsxndkvc/R0PfVCOuVUkI65zdB/db/AUVfKAbqH8dCu7dIfv+4r0uIrrSlPkeyg2G6gme6T2rTVf0Tsu933Jpq7h2Wr4NTkQsV4bNVJ/uADZqyvuspn4VfbszWq7tYat5BmalLffB3YObpj/B/ep2pGRaGxo7qBtxc/va+fS56+6NT9x4FxcAJpLzlcI7uaWT38PxiUI349q/3+AuA2fm0t2O+7n1Sfb1xk2Du+Ad8RMUTOCKz/BObl/mE1ZHg0ddU88g3MyzMO7PdD0usB6Qa7xPosCHF5+sdUu5xgKfgHMO5SchLIZr/K7FTd87UfKJvQi4rsI7vpzos4TPTD8IFySvju8pzX/3eHyprExw4ede3Fx7cIX73Bnf3aW4Ge1OXNtyBt6BdcQ1EPX8+3Cz9P64GW9ALIt34vP+LW63U3+m+1y41jcTuip2ag3U/8VxwXwcrkVdOpbdDQXp9sS/l7vI+TVXed+lgI+S35fF+vExBeYuXDA8Ap8F/BU+2LqSxOQY3+U9+fLN1ev5cPPoo/HvOpTMzXlt0Xz4IOWE+N52ouRqcFJy3q+I5v1k36OxDhSaqpPz8qbYbNC8ET4w+xQfhB1Aqb5vC1xUcK2bYp2eLda3/rjP5ru49rasL2FyjX8AmzSmDpWpS9VM1noc+H0T6k6DwlOFtJfibhuTgL0bed/s2+qJD+weinWjd9zfOZb3UyS+l3hb/SJ1oxiUnX0ej3fFJ/2NyO0vOwEvd16fmI8x5b6F5tjyeaEkvC9BI036BeVcTR0aA0xo4n12oeRb2pDPdpanuXDLxh7JMauUPkm7QdzqTZSq5p22ta3mGZiVtqQSHY3HUgTvoKbhPl1X4QJjV7yzXBoXJufMpf8VbgJ5GO/EV8OFqx3xDm6B3H1PwDuz1eLvtJFfENfgPZr/CHF3hI9z+47HfWmfx0eXW+Ohon5R8LwfAislv38NnFNUJrl9/yIKzPHDHIn7Rd2NC2SblCnf7ENcFjdpZ64TWYecmVM3weMY5tPltatr4JrJm3FBY0+805mjzH1XxoW9P6TH4vs5Bzg0l24dPETTpbhGMxPie+OazqcpMNfGd/wOiXYBFxi2wwc2+1eog5nv7r7U9d1dA9dwXhvztAk5P9xYDucnv6s1b86PN9T34QLiIZS0+KPj89+H+0bmJ4vtQPSxxIXn76kwYaXM/ZeNaRfELQhv4ZPA/oALy3kt7MW4ULxjrHsH40LZQ8D28Zx5yE2Myb3zTrn7X4cLtqdR7DZyL/UHiV1xgfhrPF5n0b3GAK/n9q2AD0TKTkLKnT8xnj8v7lrwFD6A6JI7L/uOFsEH2EWa5qVwQXWHMvfKTNLr4+3eNZW+5dy+5WL9nT8rn0rpKLWXm+OLLqTnDSy6b5m89Ir1dBlcE9+gUIRrrx+P5bEP7l++bCPumbUpN+CD9ENw/+HP8XYoK8fU0tQd94ucFrdbSNwuqDyxMnVtepS61pWKAmesM9NinXiM3Ez5oneZHO+H93mH4RNl56IRgxb8+3yWKrTWybsc1Zh3kftOxlSqoxXSbklu4F9Fmv64/+jzsVxXT99J/v5JnemHC7iHNqaet+Wt5hmY1Tbc7PgfovM77od5UfxIJ+PmtF7J+Zm/YNboLohrgo7ANYqP4Z1KoaYJdwafRGJ6xjvO3+MdcDYbdOP8NWLjnM3c7IkLSD/iDvE74g3xb4o+WLyDnUYieODC4xbx/3KaijVjg5cKjdvikwYyH6XH8MlY+YlOHSk5iD+Ia2LrzZ7EtQfbF+yvF68U1zhuQgzEjwdSL/duj4vXfjKWXSoI9qUgXA7e0J4W3+E/YnlmWrd5cA3pkrk0DxIbTOoKq91ivajjY5tL+xx1fXerFTZXxRc9SPeNo4wAlZzTARd87sS1+lfjA537gN/Gc+bABdFNC9LnBzt3UoXpLXeNrriQ/Q3uH7tS3H8CcGvu3CwEUL+C/ZfgA8rCWdK587NOY3uiQIf7it5K/ditQ2OdmW5tyNX/THgs+s7+Rd3Ocyc8rN1TsexupFigTAe9l+eO9cW1coUafXxAdntW9/L5wn2BrypXJvH/92JdvZsy0TdyaefG25M38UHaC7jb09n4QHBVyviyxvM3Tn6vBpxeZd3ZHR+gvB/v/xDebo4oeva4b85Yh9IYnuNIIhs09M3Ev4NwTX46WXY3fELp2xT7Gy+Mt1W/xQeAPwMnl7m+5X4Pjvcch/sXn51LV9b0jA/Ez8AtUa/g7fN8ldLig5Vr8P7sqVi+91DBtx4fkA/I3fthqtDgx/P/Eu/zKN6eX433wbvjA6FyEx2PxV1bTsTb6DQqRoMuDbFs3yDOiUjKvFyZppaZhWPZvh/zO7TMedn7vJbECoK3J2vhbcMmVOFG1da2mmdgVtvwTvkgfCLHvLjwlplS/0Y0L1aowFenDQ/uD3durMT1gu/HhuPS7Jr4BKSP8Ib+sdgwFAbtx82zP+Ba1JNxISf1hf0bOS1Q3N8X18aOxc3Rf44f+O3xeKW4lS8Deya/V6ZuAPPuuJBczzEeFzyewDvvh3Dtw7f4ZKEVcOH8IuChXLo/4p3ZJbhwu038oNOGthdlJjPlrrUU3rk8GMttZaqLBbkaLkTdQMn8W06YvwLYp0L9eoyCmfqU8d1NGrgFKR8O7ZpYtsvF3wvjA4o+6TUK0hUFy18U70w/IC4wUSbtQRSHRCvUZOafJ/8d4Z1wphUchZv1R+bS/p7YyJMTGJP6WdGETEk7tXZ8xnQ2cL26Tykm8Xa59IYL+svGdzpv/hnxDmyf+P8WeKf/W0qRQx6l/KClB/6NfgysmTu2N27hKbJ4zIu3Cxvn9meC6+b4gLucRvYEShal84A/J+ccS/G3PQQf3LyHD1r3j3XrNlzweDL+zVst9gCm5fZNAHauVG/jsU1wC8UOse7Mi7dlr+LuI+UmuZ4Qy2dUsu9TSvFPq9LE4QPYF0h8qZNjlwCb5/Z1xgdlh+HC4sp45Ic38W81H20jE5b2w9vMe3AhPFsp7EX8G9m5Qh4zK9U6uCB2Ct6WjY3lfGK+HmT3xq1FexLbKtyf9wY8zFzhDHTc0nEdLlT2xL+RW0hcz+K+FanvytABF9Z+wJU8q+KDqj/FuvNufIZPqRv6bjA+8Ngz1rmb8LZ9pdz1GxJY94x5zX8XRW3CfngfmpVvD1wBcyOu2a0TQo1SezAkPkM6uDkXbzcn4PNK6s3raOtbzTMwK264z1B3XKi5M+5bF3irirQnkZsVjXeotwHLFJy/FW46GIo38A9T0pIuiDdoO1a432jcBH1fvoLjJqn9CtI8AZwY/18R72SnkWgxKHYc/008bxlKPnJPUNeUPh8uTOY/9rVwAXwDXAP1H7yT+i3eub0ZP+ADyAX1prRG/Vm4G8MHuCn2x/jc2eo8eQ1uqvXqQl3t5ta41ukBEuE+HhuJu0GsiHfq8+CC31y44JGZ+/alWFD4My4I11udJpbr6xRoqajOd/fPFGuWt8A1NDfgg583s/dCGR84XBv9KqVJXnlNYua/WdSRGa6NvAPvRLfB41VmwegrzYTNBKLN8Xiij+ODkTXi/iH4oOCMgrSb4lrxgblrdYl/zyanocryW7DvSUoLZHQuepfJuWPxTjh1GcgE1hXwgVQaRq0T3lH/Fa+7j8WyPCV3zsuUt7bMhQs1/45pf09pItB92bWo2yHOFbfT8fo9Ml+f8EFi6lebabCNUluVzVJfFXgkeS/fUiY+Jy4wPBPLKnsfe+AC1Zr495R3y1kw1p/v8IHP74kD1Up1KB6fQBw85Pavjmvjzsm3A/H3Kvh3/AiuabyHGBmCKn0pcfPtkfh39gywbjXpkvR7xLqRTdQ9MNaTfdM8x/L/GNfUnhHr2brJ+9odj0IzgQqhqOL5S+KC33J4fd8i1stXqN92/pWouMiXIS6kvUtxHPCRuKD4Jv5ND8MF+pvxvuoveNs9nsR9IXeNA/C+Yo1k32v4IGkkuXBdeJuXvb/ZYtleiyuN9qdMjNV4/hZ4mLkDcFejaXhf8ueY56tj+RySpDkMbzvqTSqNx7fG27RXgM1yx4bEY+vjypXf40L24rgSI/PPrxPDuq1vNc/ArLTho91UKzIIFyo+jx9KNju+Uif8f7Ex2Z66o6aPiNqu3PkDcM3I0/io6mLqdnjXAAdWkffUjNUx3v+9gvPWjHlJG57BsRH5L+4fuE6Ze3SMH/MbsRE6Cngtd87TFKykRH1N7KnUDRs0H2WWIsVHq9fhQvEgXAAYGhu7u3CT/tlFaWP6g2IZPw8cnpYZPvLNN3xfxAbrKlyQfTk2Oi/gptDv4/HDytxvJN6QX0jOBymW2Qu5fU3y3S2470BcK3ArLszvSvnOwOL5j1MKMt0x93cdvEEeSP1BSyaILBTL94FYJr/L3aMwPFZ83//DfcO2wRvrXeOxXniHU2SOXDjWv22TfalW9k7gmDLHeuNC5Rzxd6HWu0x5zR/rwMPUNVPPhlsyDsudfwIuBNyAC5WX437qPZJzDgGequLeo/A6fjfuLnA/8I/keCYw/w63SByMT2B5ARdg9sOjgmyIa6feTdJ2w7+jIg1/F9z68jLujz+OnA979k6TPKxO9LGOv8eTWDry9SHZvxnebk4jaT8oM2EInxvwVG7f9Nin8ZkfKUiXWcf64oPRK3DXq5MpmNjSwHvpi2vV/owLvRdSLLztjgtoO+H+sIPwwf7V+IAijXyQ98d+Lq2neP+Q19YNIKeNjftPwjW261EaeJwS688CyXe4fC7dvHgbNyBXv7K/g3Gt4Xq5dGmfskmsB09Q0hg+gLuTrEvxoipZu9MdH3A+Fn/vCjxa5h30jO+ua27/CNxSdyP+Tfy2oGx/gffvN+ODtzMpuZGMje/tMPy7SevNlyQTJvFvbftY3lmc5KG48PsE9cOEZd/y+3j/NSY5tgI+MG1wuda2tNU8A219Sz6OTfFO7vlYiRaJ+zvHSrhWmfRFjegRsfKfjzde15CMTuM5WxN9/vCOaGN8lNU3OWcR3LRTdZxCvLHeOz7HhgXH8wLj8pS0YD3xDnYaFcynuDbhUlyjeR4l7ermFMy6pK4mNjNFP0GM81pUhvnyjWVxD/DX+Hs94JUq3uvmeKN6AK69nhZ/1/O3jOcvhAvjE3CtwDBKguIoXFs1kgaWN8Q77PdwM9VpcbssXnOZfF6ZMd/defDGciAuNMyOa3tuwjuJHSkfZPo2Es1lzEvWIQ3DG/LBuTTL4BqXX+LCTBe8gT0TF+QuoqDDzl3jfOLgAtf0fBbzvgD+bVRyPzkqvsdzKWkYB+LakS8oFnB3xYXyp2O9/SslYbvaWb6rU5op/jbeTjwDXJ87P7MebIR/i4+QmMspmf0/oLw2tT91zaXd8Q7+wvjOziGZtIb7iH6Eu6Skg9aTYl6fxn2AzyTGDk6eK/smF8M76uG5vOyDm6Y/K1c2BWX9P3zQW3aCSqw3vSlpFS3e63t8gFT4jeFt8jt4e7AwBa47lGLrDkr2bR/rQBqObO6Y31txAa7sqmtJeXWkbpSVfrjQezH+jW6cy2s2eervsf68iguor8f9K5W533bxeDon4lFKqyVVajezOQhv4QPJl/FB07Fx/9lF7y6m3SWecxL1V+LL2uMryfn0lqkLB8Y8vF7uOeN5eY1uFnbxrViX1qzimYtM9Cvig8TfFRzrSn3hdUCsB/k2OnvuPwA3Je92CN6fvBjTPZfVW7xNPzO+6w4xbQe8vd4a75MWyt3nXuC0cuXZVreaZ2BW2XDB5EDc1HIB7rt5EfVHavX86/AGdx18hm8W0PdXlJbp3JG6yyR2iB/f6hXyMwpvVI9p5HN0ws0I9VYzodh0/wwlE3HWEM9LAyu3xPNGxDzeh2tkPyTnl5WVExU0seU+SOoHeN8I17z9NTZg1fixvUQ0v+Bmlsdxv6xpsXHpkTRC/eJ7mQOfcDE2nnd+vh4U3Kdfwb5u8bmfwBv2syiYEU/TfXcH4R3Oq/H8F3CzVRYubXXcbeJ+cg1ico318EmCF1C/U7oBuKIgzaWxXC7DBYtMO9OXMh12/vvBO89j4v8vJnVwf+r7zHbENZerJ/t2wN1Hfo7lNgFv5LeOx9fGBZJ++Pf2CT4YHIYL9U/gws6iReWS/8aT3/PEax+Ba7ZWoH49zQ8Gz6Bu/NLFY9kdkf92498t4jv7Hz6w2ZuSFnggrh26GDcRHod3mDcStawkGs7ce+5b8Hx9cc3ePLhw8DC5iTr4t/A8yVKUcX8vvO5mFoBUo7Y/PsjeLH/PeHxLXDHwDD6gu4roc45/fxfFOrZnmfQL48qA8TEPC5K4uOBt+b25NOfFa96JCw/rUPKjXJyS72hRvc3axtlwDepEvH3YiigM4yEFd6Ku1nwgrrm7H/9Gt8K184vgJvidKNPW4u3ri5QiCewAvJr/lorqLF7P/4b7k14Rn3NFvF86lpzPc8E1lsPbk6/iu0z9uAfj7gj5hVGysj+eugvB9I9l/zZuGcu7RGVC+7q5/fPiVomHy+RxflzreR9e/w+lYKlRCr6HMu82+3sycGOZczcEHoj/L4u3OTfiMsAiMb/b5p7tX7ii4oEKeZgdb0vfS/bJ9K8tKURviC+I/2cfW6aCn1bwAXWirln/UrxDeQDXiJ1Hweocyfl/o76GdQ9KGp6huOnqr838nEUCY9rwVQxVUuaahgupkyt9iPHcfrGsfohl1I0yAiDe0dRbFg8XNj5t6F7x3AVwYWtwvNen8b3Ojgt0v8+d3wEfxW+a7FsB1+58S/l1yTeJDdZqlF/pZBE87FneFN4k392YNlt7eiO8M9gH16I+SNSmx+fOT8SZH9fWbIV3+NvFNC/iWsosfu9LFEdC2IRSTM9/4zOIt6akGRtGrsPOpR8Un/vZWB9eTo7Vm8CFm6v/FfP3cMx/p3id9fCOdytKs/I74IL3E/igZCeKBe7bKaNBo762KO2osw6tf/YNJMeKrAePU7IeZGl7UDAjOP7/aXL+vfig+UncOpBFKFg0PvceuPB0L1ELlNzD4tYJF66KzPsjcYHkmFydzISUzB1jzoK0V+ADsKIFIHrigtz/KJlDs4H9dvjg6kA8YsiO8V39QLISHS4I1LMs5O6zQSybZ3GhZUB8/5+Ss4Lhdf0hXGgchwsVf8TnInTAv5XCpU4p9Qu34N/YKrjl4zN80LAW9ZfAXAb4JPk9BnczeJYyy6Im7y7tX7aJ95lGwUIUBdfYN6kLq8Z3+TnJXAfKuK/FdzJP8nsn3ErxGtGEjQ8qLsuly97thrjCIrt/j+TYciTh8+K+rrjFIdM6302iScf7lu+As3LvIR+t5Jr4Xu/Gv4nOFCww0kC5pSHAPqZ4sZpBsTz/i7dH1+be1VjiEs74t7co7jbzNYlgj7vgpeW8Kf49bZTmZVbZap6BtrolH08PvLG+k+Il57am/vrEB8SPYxHcVDweN80NjR/qJbjAcyb112KeFzfBpZX7T+TMh3F/Ref4GXj2ftQ13ZcVGBtxzekrvlRxbqaJ/Vf8QPNrlneNH3ZReJeulGbt/jruq6RRHRbf8WZE/05cUP0PxULYmcCx8f/UZ20bXIh6K58OHwAchAuVp+Mm1C5p3nBtwrEF92uS7y6u1fukYP8QXHj4hAItaqynz+OarHdwIXMobsY/EO807sW1KGW1jXhDeyLeYR8Vy/NcvMMuCreUlcMfgZfi/4fG578mXuMScpqMmK//4gOGpeK5u+HasP+jQIBK0m6MT/a6J15jG+oKh0dSvIDBXPjg4CViuLa4vwOlcHQL451Zv4K6UMl6UM9vN5f+QErRN+bABZQF8PZkGokvYsxPR1wQ/TfR/5piofoSysc3zvzWj6cUGaADpYk646m/xPJy8Vif3L5D4/vJNMA3U38BhE+o39Z2imnfpMKiEbirwIr4QC5dqWufmM9r4vu+rUz6xXA/zSXwgeW9eNu/BwWxpnNpl8HbgKwuP4y7YDwWn2nv3PlPEX2XqduWnImHUrqFgm8M/4afwAeu6XyFw3ALwj2Ud4vohmsYX8+Vz674N/AGBVrHeM78sTzux7//9Fs5A+8vskgD5Qahr1IKcbhBfB8vkVv9LJdmMK7F3BUX+qfh/VI2ENyR+qG4KkUreZ8yA4F43m/wAdJKuCZ0joJzTgROLZN+dlwuGE3dUFxZ354tVZzJGHn3qgG4BSiNWtONCksZt/Wt5hloq1vS2NwUG7jx8YM6sajCUFfjsS8+Mr8ebyAvzJ07Ah89P0j9Uf3FuCYq75ydjrYOpIpQS81QBhUFxpYuf1wTOImcpjKW0T9z++4gEfpxc/y5Ddwj7bAXxTvz03F/vfzCBtnEge2AJwquNRDXNJ5Q4X798AHAi7GOzBv370ixUDkjvrtHUprV3ImcAISP+otmRF+WlS3eab9LFH6Sb6LBZRBxLd5ZeKfZF9caZBPW6mmqs794B5PN7u+PC48H4R3aBtQ3oz9NMpkwfhuf4C41/43H86bEtIPtgnf4j+JatJ1xt5oBuCCxV8GzdcTNtYfiA5N7yYUgwr+ZK6uoC9VYD9L39muiwIMPOC6O/+8R89M1/4zx90m4RadTwTUXxQWsvMZvep2J7+EB6i9eMYACIQOfHHRi8ntNXGv3VHwvj+FC5dy5dL+iNEkm71bVCxf+LszfLx5fD7eQfIJr+KbFerBaUicvwIX7dJnpVEjsGOvDB7h/fjd8UtsbNBxzeEvi94+372/F/zfCtfPLJOf+Bvhfmgfqru43mJK2vEgRMg1vm++i7uBkYCyDOhMXC+rvFbHepQqRPpRCPdWbgxDTLYMLqffgAtZmuTzfRfnQe3PiQvKq+ODqQ3xAcXp81iKtfNZ2jcHb58XwQeBz+EBoX/xbSv10q41WUuSrPjg+/xdxewv/lu+I9WIXXPk0mgbcgnLXXSE++6np94n73/9MErIP17pekfzuRi5s26y21TwDbXGj1HEuhgsV3ePHsSOuYbqv6EPOXWNOXAPxJD6K+h11Hfe7UX8Fqk54R3sD3oltFe91ZnJOf9yvqDD0RQuURVmBcSbdvwt1BdCFge9z5/yZ+v6Zm1J/GdqscRiC+3SdjQtGw3Ah6VhcUPt7Lt0m8T2sgmvXJ+Ca8U2TRi8bzBSa9nPXWxjvqO/FO6yvyPn3Zfmlkb67lASL7XCh0HLPnmlyzyOnwY31/YfcvsspE70gd95AXIuwGd5xdI/7D6GutmAnyptPD8A1r6dQnQ/0xninslhyv0eILiG4YPIi9bVzWVkMS/YNwwXrx3Hh/DHKaExy72epWJYfUHIPGoW7gjRoVqSKwWDyDv+AWwxmi/ceSzQ/4hrwrdPzc9cYgmvR3qY0SbMLruX5DxUWwojndop1/TNKbiOVopvsjWsE58Eneb1MDAuGa4JfozgE2wLkFmWgrlC9A+6fWiRkfIhrebN4p8vg3+1kYKvkvFTLtSKu6T8NFz6WxDViG+MT07J6NYICzVru/p0pmbTPJvYReLuSD9o/Afdj/St1l8buTF0t6fAy39rVuBZ8f9ycPZY4/yGeswoF7mWU6v7quBBWFEd7fur60ebdkfrglozTiAMycvGMk3OzvrQb7id8FK6Rna5FjOX9JsV1Px1U7ogPPDKf953w7/+ANK/MWLSS3ng78BrerqwL7IW7403DBeRpVBdpZw3cxaU/LlBfXPBNr4Vr7C/BlVrbAJ/mrnM5OX/qWW2reQba8oYLl2nl6op3QofgjXs6Qi7qHPrGj/4CXMN6Ij6aLAyzrGc/3gAAQDtJREFUlKRbEjfx3omPGreg1GBeDVxSg7KoIzDW8J0cHBuK3eLv7rGMhifnnESFESg+Mr8RN0E+BzzbwD0z0/3ruKZ2fEz3OqUJE4tRhaYxrS94x/gt8O8Gzu1Hldq3JM2yuOZqv6I6ivvB5de53z2WbaYV6o5rFebOpy+43zUx7eu4YH0W3oldjwsK9cLjFFxjV9xq8QZutq9o6orl8k+8UzkYn1Q2PnfOo+TWMM+VwdvUDey+Ii4IPUv9aAZd8U7uCOqGn+mFa/Puxjv/aRTEJ26gLpSzHmQd2sq4L14qxBwY991DbhnWMvcZhgtGX+Lmz4djPb644NwhFAtJJ+MWjYqLYOBC4j2xTF4G/pJ7nkcpHpz1j3k6hboTTLPIGsdRECkAt2Y8UKauHxfrSNEy0bfG9/VIzOfpuJCTaWR3rfCMlb6HffBv+9D4jpZMjp0an3+lWNcyX+lUgO5K8UA0E/zWiPn9E+6bfHG85slEX/V8/gp+Lx+vsR+lxSmK7jkb7mK0QqwXQ/Fvb35cI3p+fGdHkgsXlrzvS2Le5o75nZ/SoPle4LzcPXfDtb7HxDKcH3cdOQZ3p6gXrziXvtHRSpJze+MDnCcpWb12wq0zw3HXn3KhEjNheGvguWR/d0pWsby1YxjutnEDPoC5MbnOCJLIPkXvZ1bYap6BtrrhWq/H8c7jQOpqQ2cjtyxmciwbxZ1E4k+Ha0vuwBvGIyhYxjFfCfHR1vl4Z3wkrqX4klkshloj38uclGZ0Px0bnD8mxwdTYT153LT4SvL7dUpaqe3T91yQtl98H1PxDr9q00+Fazab727B+bvj2sErKC21Ox+uhXmnTJo1cC3kl7jG55i4v0u5RhLXtu0b6/fZeIe0Fd4Rnoh3/ktTXezJzrglYgLuKrASBVpq3ER/SszXSnhnPw2PYpCutvNxA3XpH/F9Xkfd0G9F2r7LcdPmK7jPZqbBzPw2u+Pal3qCX5Xvt+xgENfgHxz/zzqxzDViT0pmzoqTLHABaGFcONkrvpe8T/XBuH/tg/h3dkTclsE1TBPxzrQoRNovKLktrRzr4LLUFTp/BXxYIY974v7Rx8TrZe4Mi+K+6SvmzjdceDk+e8a0LHDh6EOKI52sjQ/+zsWtFlvhA481cK1q4ap/uffwK7y9vwcfbGURBk7CB3BjkjQDcAtbFsFgAVwIuglXZtQTjCk/e38lvB2aL+Z5R1xAu4P6wtCc8W/ejWAHXFhdrcJzPkRJmP8iXv+rWAc+xjXg04hhmQrKp28s4/xkz0G4Mug56kaE6BjrWBZi7la8zfs7rpSYhrsB1XNjSa7RlGgl0ydX4cL4PZRCR71DXHUyf88kTbrvHUor1ZVrN/PuWEvjbdpNeH1eFf/+jk/vMytuNc9AW9jyFT02Mt1wzeYZuAnrTLyzKKs1i+ffi5tCJhFH8JRGlV1w890DVDARU38Fm9/imrvPiSvltOctNhzz4sL7hNhwZua+68mNznNpfw2Mi/8fDzwe/x+Ma1qrWQd+flwwegAXGGeakztVuGIk9a0n7lN1Ky54vhrL63YKJqLlrrErLsS/Sl2TYiUt0pK4OfA63FS2VqXGNWng++BC0GbUDeVzDd4x7ZVL1wGf7HBbfGfZ5IwtcQ3pbbjp8x3qCgnlOvwlcXPkN0RhsOCcX8bvL9Ns7BPr3QG4AHscLix0ppnX4o7lMg13L6gXCL2F6tm6uDBwePymbsA1hFdTEkxG5dJksS03p3giYhdcaH0N2KGK+vcVLkTdhPtU/4tkudbc+YdQ3/3HKGkgLydnQUjO64kL1FfFOrcfZRbCSOtg/PsLPILAfnGbRuWJQcsR3RCSa3SJdfAQXDh6sKBsO+NCzP/Furhq3H9GLKP5cQFvOXJKFFyx8h3uzvJPXLv5a1wQ74b3be9SPDjrjAuZX+JWnQ3wScLz4daHxeJ1fkmZeN7xfm+RxO2OebWYLrVOZG3X0jGvr+D+qavgA8tF4/2K6leTo5VQvLreOvg3/zVwc5q/+H8PioXkBajrrtfQCmp5BdWvcQH7SeDtfNnMilvNM9CWNmLQbdwck00o6RAbh8vxRux0yi8R2JeoLcA1LkeT8yXF/WKKTBVFJperKGlKhlMw+aU9bfkPNTayy+Bmr3fxQcKX+fKlJBANx7UdD+I+Wh9RWrjhUuDaxuSFVuS7W/S8ye8Fce3mPrjwmI800RMXTMbgmrPM1N8b12L8EMu2UCDHBbR0+dmVY0N7Gx6BoSh2Ydrg34trKj/GfS2PTI4VTlrAB3Ajca3gw7hwvFSsE3/CtaRPl8nvb4AlCsrgP7igUW8yCN7ZHZb8XgufBHEcLiy8RKJxaY46Tl3z5raxvr5EsjJc/ptohvtn2ujtKU3UyvYNiO9jDXIz2OPx23GBIIvBnA34F42/B+Nm7xMr3D995s648HZc3BaijMaYGOoJF+bmzB3rhrcLmf/o9BBGufPmw30ob8HblD3L1fkkzRWUArAvhLvbdMQFzw1z91sI/wYLBxv4gG0NXEuan79wZKybk3FN8234oOAP5FbrKnPdxfFB617x+f4d03+FTyKcRrJEdsE1lse/s6fwwdns+bpXVBdxDf4e+PfzEbnQcgXnD6Su9XIMLqw+GOtkoaKIGYhWggvyV1FaNCDV7q6PD37rKYhweeDh+D52wecy9CiXxyq+vfS+ffHBU7Zi2CwVjqres9c6A21lw31Pn6HkF9ord3x23Lw5lsqr4nTEG9XdceHn1vhhd8JHaK/nzu9LXZNj5rezP3HWqLZ6ZVzkAP8r3K9o56Jz8U7y6dh4nRwb5gl453RkbIyqXuErfV+0Dt/dVUi0VPgAKz/btQP1TX998U7vRVxL/Cwu5KUB6BeKx7bNpe2Mm+7uxzvoO2Pd/0U8NiZ+L3eTm2xBafBwVGzsM/P59rjG7lqq9MfCNdsv4B3v2fFbnTtuqeZrTnwizz9xLd2+JEvY4p3Nbwquvzg+2eOvlCIS3EcyAQmfhPenfN2cgfe5PT5oWDC3/y/x/dxAGfeW5viuYhmtlfxuaHWuX5Jzs4h14WFcM38NLjANoL42a20SgbBM3S30k8YHK1nd2QfXGv4NF+x74dEG/kkSZizWgeFx651/Plzrezpu8p6/zPN2wAerJ1CKwvAypYUpDiQX2gwXZD7DtaabUl4D2bdg38h4r1fxAcFceJ+1CS7Mr1JU92K6MeRcmnCBcHZcoN4F9++eJ18XcEEzrRe74hrxB+MzVJxglqQbjA/o3sUF5Xoxe+N5R5ELyRf3Hx/TXocLj/mVqpocrSTWj0upHyYt89s9Fu8vVk2uNSi+y69x14V78IHSBFwTeh8lOaAal6e0/s2ymtOyz1/rDLT2DR8Bdcc77OVxDdln+Mg/P6rtR8kvpmzDnVTm4bgj+PV4g/0pyezTeM6R8cNNl9UzvPNdPf6u46De3jaiVo36mtI0fM5ICmIHJsd3JU7oiL+Xw7UKd8ZGsDB2YFvZcK3Fp/F5lkr2d0rq7HPkVjLDBbarca1CZ9zvcZPY0P+HXPigXNoLYgN9GK5h/Btunr2VkvZqBDFofr4Ox/d3PrmJVrjp7BkKzK9EX8fkvS4Tn3sYrhXOtOtrU3eQkpmku+Emy8NwYfhyvKOeDe9kiib4ZIsxnIn75d1KLpwY3jHNUAgZSkL1H3BhJDWVrp78PzQ+z0fN2S4k5ZUNstfP6lDueJFANJoYjgvXTu+Ex9X8Pe4H+WyZst0CF0yOie8lnW2eCaDbEN11cmm3j+/sMKK5GxdQ78K1a9/gQtXfqRuGKdMgXhXr2VjcbL87PrjpE+vm/AVlU2ciIy4E3kvUqif7P6BAwx7Tn4Zr/S7G/UyrmnOA91Fr4v3FBOq7xBS9l+dxP/HCKAoN1cUyx7rigvw7eHuTD/+W1ZfZ8XZ7o+RdLoN/c1MoDmu2LD5x9T58AJ1+A0Pxb/Y96vo8z0i0klWIWvBk3ya4u8OZRHcIfACc18SOju9+TVxhYbi7wY/49/M0uUUB8mWfr0+NeUez0lbzDLT2LX5w2bKm3eJHsinuZ/o4pQkM21B+Akr2YS6Pm54uw2c9r4wLCivHyv+bXLoOuP/Zn2Pjcw6lSS8j0mu31w3Xlr6Cd3S74EJH/mPvgc8UL5zchPsuPU4iqCbHGq1FbY1b7DyWpBQq6bxcY74RbjZMG+RFcC1AUQD+pXBBtdCXEO+AvqH+Ygwr4tqW5ynvmvAbSpaDo/AoCr/InfMEcRWWZN8msWNYnZJ5+SlKWqxOuGC8Az74zISLzCSdCiv9ca3XEbjG913guoK8/g7XYu6Ld5R74YLOI8Bv4zmrUGHSVpXvz5J8fUXimkBpXfZ7gRWS/dkEmWabZBHbpEvj/e6nbqzhSv7Gi+MuIsfjFovnSCYGxTI8M//txmNb4wLPbfFZF8nV068pjvl7ZLznVbgwujOuRe0R87MMOWETH/SfSklQ3Rr3Nb4q1udP8bbiJQpCpOGDsz+RhFiLdeuT+Hy74oOv23LpOlJ37kEWveQtfNLVglSw1OXezyBcQH4W/87qrZAUzz0Q+E9u3xq4tW5fKgujJ+ED+f1wTfU6sW7Ok5wzlMRNJ1eP++GD2GdxLff/iOZzvK0aTZlJpLjWfU1coH8MF+hTP9Z5c+fvSdOjlZxAKWxaR7xt+gYfwD+Ca7CHUz7G8R9jHdwp/v4zcHWF+2WWpC1xBcF1+ECpxeOit+at5hlozVv86NNVf9ZPKvcv8E7qblwIep/S8mXlljb8ADcT7IaPpv4W91dsgOIHvwneYD6Em3JmyqSJtrLFRnc8LnRsgGsXso/+YmKQ8DJp18Y7wm9wwSjV2sxSMykphUq6C9e6ZELcG+RCJuEDqNspaTvyA4ALgavK3OdoSmvHd6Su6apzfFdp7MpMW3gOdYNZ98K1H+fj2rCFcOHho4J7ZmHC3sI1cHXiyubvFf9fIX8tXJj6V/xG743nzEN9t4i5ca3lKiRCCz7R40jcUnIxHtd4TDO9v7NJFgrAB88/4xMqT8KFtn3y76oF6tHysXwm00DM6CTN6JjmPuprkh4B9s3tS9vO7rjw9jDegW+LDzxOoUz4OFyLfifutpL5RF9ABSEoSXsEPrFmnvh7SHzXB+JCxNoFaebFJyU9Fevx7/GBcxbt4eyYh+2oG2qqXl5wbeQquAbxsVin64Vwi89YbzCNC91DcfPyZOKgKfetPEHdmep/wP3An8IHoedT0DfhGvG3ceHvVXxgNgkX3rOFMQ7DrTj5NmOl+PcaPKLGQOKiELjCIVsdLZ+uK/7tpyuZzYUrjS6L9ecUyoeFWgMfHH2J99XHxP1lo5XE41vjwvTK8f9XKCmnuuGDls0K0uXDn92MR114j2TQnTsvdUP6gtLqVe8Q22ZagRtZLbaaZ6CtbLiW5vXY2KwVG58OlFah2LiB9AcCd8X/+8cPe0T86Lct09jUiVuHh+S4Bu+4r44NQZMcs2eFLTY+2ya/B+Id0YTYyC6ACw1TyYU+SdKsjGv/5sO1NXfjwskWM+MZalRuFhvoXfAOaQrwfsF58+LagI1z+zPBdXO84001QakrxRO4UJrOrs4m3owlmsKTNHNTf03rLHj4X3Atxne4z1hZDQONWOIXnyFcZJLen9Ia8luk+UzS3khJGK8XYxIXTv5KhRWoGvneOsX6fXDyex6iiTc+9xH4IKE5Tf7550pN27vgk0M/o8xCDQXXS4X6jriJ/r1q7o8LX2fiA/aLcGFpyQpp++Ma3F/hbcH5uCB1ErBe7txDgD3i/4vFb+OR+Pto4M4Gnqsnro39L94+/zPm8deUUUbE/D0W790j7jsRuD933mHEyTzJvr1xTeQzuBLjIFzDuUAsp+y7WrKg7hou4GX+szvgwtHGsR7thAudhT6msXyuwb/zfrgguQke+u9f+HeTfd/Zt/9X/HvshFtVlst9/7vibW/P3L364N/aUzGPh+aeY6GY9nGSiYTx+CjqWo5+i7t7VBWtJJbjJbEsXiFOSkue6TFyVqXkebJz5ovnTSNGpSh3v3jsL8Q5ALjrx2d4+zIEtwpUjLM+K241z0Br3CpU2nXix/kA3nAtSX2nfsv9TWfJnh3/v4XSjNlf4VqC1ITWnRjbjZLbwIGUlg5cFdf6PEhi6mtvG64BnYZrTdZI9i+Lm5VeiOeclUu3ArB5/H8qJdeO7rim6JjYMN3ELDQQSOriprHR64wPlo6n/hKfc8XtdFyrOpL6PsAPEVd5KrjXirgFIZ1wM92XOr6zQ+L/2b67qBuEe05c09shvpu5cQ1SPVeEMnmoZlWnhkzSf6XAJI37rN5Lydc2L8x1itdcjQaC3zfyHZ4B3JLbl1pw/pyWYTPXnz1xAexEvD3rGfd3x7Vjazbyep1wYet5El/DgvMyy8gAojAcy/UOCiIEUH8Z3dG468aGsS5tF+va4ck5A/BB0shc/cv81D/OfyMV8nsorkVbO9afO3F3gF+RC3MU6/QhlOKb7oqHPBoYj5et6/gg4VvcPJ0tM/ogpRWSXsMFvHKD9J1whclEvE87KjnWAxfMFipI1yFuB+ADx8xkvz3wZPy/S3y/+UFoFiA/i02b1t2OuPZykdz9rsd9T9fEhfG38UF0vu8dkvv9ezzqwkq5/b1xf/mK0UqS80fE+jYfdQdpm5MM8KlvARhMaVDfA+9PLqN83NSsff4dpdioL1Gyeu0H3N4S33Zr32qegda8xYZmr9ggpB/Uzrj56ibcXJLOSC2cUIALue/hAu6Hyf4nycVmxEfP00hGpPgoPfV76ksygaI9bUkZH4ELpOdTmjk8NDlvW2J8u1z6bIWlCeQ0F/F4P1w7sGmtn3UGy2kV6o/ue8RnT+tSvsH/Ha4JOhhYAhf4J8SGcsO4/Ql4t+CehxAjK+B+2J/gJvjMZ7Q7Ljj8j7qasvlw38vjiOHdcMvB2BmtKzQcV7ZRJum4vxMuxGSmynSgmZX1ZVQQwJr4PGvEMj2c+pNU5o9lOHeajxm8XyYkHhDrwZm4VekNXGtdzwTeiGt3wgcK9QLt587LyvN2EmGq6Bljnb8rtgXb4D7+8+DuQPdT8u2fm7pLL19NSYvVlZKP9Ga4ufi+RjzXfDEPB8Tfm+MWhPvIzRyPx3vgg+c/4sLh+ySTFCkfcitzi5kYnzETip7AhbvdgXMayOvI+G7np64Qdno1z4xb9Z7AzdTvU3854qz9uZ66kRU2xN1/zoh5XwQX8J/JpV8O+Dr53Rv/Vu/Ahd1HYn28ibqa+gF4G7MmJWVPZxL/VVwT+yQV4tpWqLfr4n3O5gXfymG4lvsu/HvZIO5fCW97t07SrEZuudVY596PdebpZP8HybVmyRWoypZ5rTPQ2jZKjeLheMM8Dh8R553C++IamFsp9jM5K1bUxZNje+Mj+1fwzvMvxBFoQT62wH2iXsE1rlfE/Z1oRrNeW95iA3cb7iu5Gj7h4V2SlagqpF0uNhrTYoOXTqSZizauqcYnU9QbfeMdcragQVG83szvcqVcw38Srsl4GtdSn0n9FYAyrVQ24W82XEh9NDay18b0jxDNZbgWZTNcQ3Iwbma7Cu/A/pdcu17A7UaWR4Nhwmi8SfokXBNVbwUc3J3kQ5pRm5pc+4jYJpyNC2O9cb/JB4BTs/w34/264MJx5l/4D7wjvh4XWP8yo++nijysgg82MmGgnCn9Qtwn8yVcMD0M1yzej/sK1/PfxV0Csnagnqkb18Z/hgtl6eAqa+t3xBUQ6+H+hXPjJv37kno+Jw0sE4y7Lq2Hu3c8jg8GByfHM6FvkfRauB/0nfH/1XCfxib5MsZ8b4lrkPMTGH+Bt43T3VzwmfvXxfIblzs/Xc4182fdn9JAdAVc8/ti/HsX9UNAXYW7YCwZf8+NuyqdFuv+4bhv6u9z6c4DLkx+94319R1cgC27oATRJaHC8Xnjc+xXcGx4rH/rUPJLvhG30g3FJ/ClrlLb4EqAR6kb/zhbUOU5XEFwHXBNWg/a01bzDLSmLWkIsg53AXy0OwbXnuyMm6kOSdL0jX9TjWsvXAOaae0uJTaAeIN7Bt7R7Ez9OIh5DcHRseI/TNQGpHltb1v+uXFT8DhcKzAI7zQexgWjvL9SJ3yA8Iv4XjeMDcuTuAntwHjeO/mGr61tsf5tlPzugWsX16EgNmRyXt7vMm/uX4/ys3FTrVRmxuqFd567xu/g9yTxPXGtxgXx/67x+sdSCh6+YlE+W7jsqjVJD4mdydtE7Tsu1M2Ld67HzWA+so6+N96pzxffY+dYjndTCrP0NHXjtjanj+oawCXx/1/EutUX1yr9u1IZNaXsk3fQi9JSoptRctcpG+kEj0ZxGG5yvxwXBDrgKxEtSy7Yf0zzFC7EnEuM5EJd7WJHXHg8siDtfHg7/1+8zfkLPmC4K5bN6xSYlpN32xFvwwZS0uLOiVuDbsctRvkFOt6i7uISy8f7jI314fcNlVOurAdQChy/DB4hYZ/cuVkIt03JRTvA25Ur8W+2aOnvl3Ef2PXi/4+RRGmI72VB6i80Yvhg8Up8kPt7XKg9PH9e7ndnfMCyZ1Ked+Lt/CZEq2aZvG5Amcl5ufO6UzzRbAR1v8N5cZeZS3EXpIVz53fC5YxTcFnhRkp1fkm8Ll+Nt9t907rTnraaZ6A1bUnjcT1wbrK/b2yMjqUU3LfsSh8xzer4KGgX3DRduHpFhfTpCix98ADeP+EN5kxbkrO1bfiodCHcPD8k7lsRN/Vlzvnzxve0UC7tObg5dpmC6+6IdzbvECe9tdUN91O8MbfvehqIlUgZv8vYYVhsVP9MQTBuGtBKNSLfZ+KTpzahFJf0z+T8zFq4/KoyScdzh+FC0Ze4ue5hXAty8QzmIQ0cfgeuuXwTOCU5Zw5csFiCRABr7o4M15plCxkcSXTHwCci3lxUl5rhnhfjQtr7uDZ0kUak7YYPQk/FLS7nUEZLFtvn9+P/C+IDlDvjd/B/yXk9KV6Wc2hsay7AhaPj4jvbEBdQdqA4vFv2fs+gFJ7pAnwyU594bFGizywlTfIOwKsF11sUb7vexAc21cRCzfLwMFEwxQdafQvOza8qZriwtmT8PU98V3kz9u+AT5Pf2YI37+Lm+lVpWKDO/HjH4daeMcQ5HBXS/AEfdGxPaSJWlvd++CBitYJ07xKF6IbylaTplORzu3jfVamrff8lie97wTV64v3Y9fiA54/MYhFnZmSreQZa2xYbmWl4A5z5el1CsnwmHmOuMHRHPN4R13ychZvuN44f1zPxg2lwRnnSMA0mTgLBR56fUIMlOVvLRmkd8VNiQ3c97gP2VPzAF4zn5TWBi+PCRBoaZDZ8QDE82bcUBZNu2soWG+EfSSaZ4B3p81Wkrcbv8hJgk4K0DWmlMg1rpdmu/fGBxH9wAWJF3OowFvhrrcu2Qr674hq7/XBz39JUGaS9wjUzIWJcbGs6xbr6KQVLuM6EZ8zysxkuOK6L+9w3S17idbPloPfBJ9WshQ9KLyVZuraBOpS6X8yOayYviG3FGeTC+uGm2dWT3z1woeKPuAB3JbBqpfvGd7Me7gpyI+4aUW8wnJyfte3r4y4FS+CC5ne4xvEs3PJWtL78u0QLRJan5Ho74ZrCHQrKohuuGa8TOzTm4ZNyeY3nNLSq2NW4oLUW9S2EHZN7pssoD4vf9Vu4gD9b+kxZueautQQ++LgRHxxuSZnJZrg2/gpccD+Zuj75w3DXhoG5NL+jfqi6qgd9uLb3YdwaexXeL9WbyJY8X7n6tAXu9/sKuUUb2utW8wy0xg035/wLD7VxUWxM0tiaFwKXVXmtLGh4Z3z0fQzua7Vj7rzp/j+5/S8Au9W6TFrDFhufa/FO6wlcONgL9+X6S9xXuFISrnE9Ifm9GG4m+yBe78xaP18zldFg3C/0DkoxGz8j+rrRgC8hDftdfkDO75LKWqlGh/mK7/UJ3P97I9zsNU+ty7YG73IJfMJJOmDYn0Rbjpu1523m+2bvfl5cg7c1PgAyfCD/OK4FPLeZ7rcSrjE+F9ekX0JukhY+YLmHClE4KAlseVPwArjZ+CaSRT9wt5/Mnzrf7s6GC3GXUTAhMzkv/T4G4kLGRfHbO56C1fCS8++jFA5rO1yRsQkusD5EFHIoDRJPw7WW/8X7hbUKrnkMBaZrfLBzIfVn1O9MAy4VNLyq2HPkVlSsVF7U1TSuTW7CJD5Y6Eb5JWQ3wAcf/6ZgERfqCr1FWvD7gdNz+7rjiownYzmvkc93mbxk72ZjShEPRuDuE4/hg6MNyC25nrvGhvgg91RKfuB9cHe2z4F1m+M7a8tbzTPQGrakss1NXRPar2KDMJXohxUbt28ohY9KHcu3w0fia8T/+1BaweeP8dxuuMYlbeDq+P9QGnFtDkxIzmtwZZL2sOGapRdw7UG6hF5vCswlsXE8g1KYj8VxIfUKXGOzOT7yLgzj0lY2SlrLbBnQP+Eaj/epu2Z9pQDXjfa7pGGtVL0QMVU8i+Ed5Ffk1vVuLxuutbwmtg9pG/UCpYHHeRRM6mim+7+LC1yf40LUmOTYAJpxVTzcheFiXHh7OD53Oiu/N65tXKUg7Z659nS6wJqU24JEoTT+ztrc31CwwlRy3nwNtQvUDbvWJV57T1zzd17u3Cw/PXFBdtH4+31KS9L+hfo+okNwTV235Jyp8R6plWgA0ccx2ffL+A6HJfkcgFvoBlXxbpq8qliFa1Zqg66M97so1oc/4oOlDSjFZe6Yb1Pwgd1x8ft4Ax90r529X9zn83JibNxc2rF42Mh1cSH4lnjf+RvKc3zn+1F/+ek1cIvIQ9RfLCSro/+Hu2zcjA8kvsbrft+s/jXXN9aWt5pnoDVtuOlyX1xrkDZ8h+Ha1XvwxvKUuD+dQJWtEDMNN/2cjjtHn4jHtptKmdE19f1/MoH2Kkq+Ye1eSM29k46xcfg6NkwVg43j5sQJuFntYZIZqrjp9gkKNBRtaYsN7G8p+bgNwDWSF+Oj++PJmbvKXKdqv0uaQSvVQF4anK0/K2+4pSUTbrLJNg/iAla2IEMWc7M5J1BtANyd/D4wfj+34QP45hRS0+/6/3DN58u4X+IoSuGbPiuoYwNwF4Qvgd2T/R0oDdwWwk296WpQt+NCfupz2Y0yyyw3kP/s/WwOXJ/sX5HiOKRdYp564O4uIygtBtMXN4cPyqU5nJwVL36n9+HazeMpE2ECt66kk6+Wxl0rJlCKw1qx7tCEEG6NKbvkd2e8HZuGuxQcgEdwuB1v69/ArTX/oq4rQTbwuA5f+fHEmN+HSVblivU6H1lgflyQzwYBC+Pa4qvwRRv2LFe28fwNY36/pXj+Q2Gs5aTspoecwiM63MkMTsSc1baaZ6DWW9LInBE/wn7JsTRkUbf44aQxUPMmpo1wc8RkfDWSvviM5/XwQMVFTvVF/j/HUlrdYyxlzNntZcMFydRclA4QZsMHBlPiB15pRvD++Ezp7am7JvVKuHa2Tc+mjHV4fGxcV6fUUQ/HfaTvxAdb1awZ3qDfJc2oldJWr8yKOrVMG3Ycbp58iOiLXKneN+WeuHbqzNzxAbi/6Pu04Oo4uLDyh9iWPohbO64DtqyQZhfcJP4aOa0rbupNl51tyOfyWipM1qGkDVuNZEIOLizXW04zOb5x/Pt34uIv8Xd/XDB/GxdcLk3fB96PDErO70Tdybbr4ELn19SfOd8RV4JsGX+PjGV0IR6ib3e8n6lqMEgjQ7jNQB04HZ9AnFkO5sXb6L8SV43LnX877t6QDnr649aen6kcjmpF4Nfpu43/r4C7UlyHuyEtlxzL3s2iuLZ3rpjfKbh2uZzbQupTvAquRFgvvS4+sfchfLJku4zuU6/cap2B1rDhGtTPqRtkeStcw/oF8Kdkf7bMXTl/nm64JiBbSi71iSqKW1nO/+cPuP/Ps+RGsO1tw7Wg9SY04Fqe7KNfmmRGdNzXOe7fndIqOnXiIMbG4EXiUoJtfcP9oMfi5qQ/E33SYqeyDDEMTTPdq9m0UtrqlGtWp0fmO+S4fxliXMpk3wx3aEnnuw/e6X9NwdLQFIR4aoZ7rxqfaUyybzguQE6k/GINqWDRL9b5KbjJtWP8/r/NndeQz+Uz1bS5uPCSLW6xPjGgPYkwkpy7Ju6zfVHMz5y543Phcxl2o76W8UJcozp3bn+XXHu4Tpl8HoXPizgbF9pvTY4NwCdCNipuNFWGcJuBur88bin7Z/y9F/BwmTRrUD+yQCrIn0p1ixfU8yPFtd4bx7KrZ43CtdVnJb9XwQc7n+CD/PT9TJ/IFvN4b6zzt+Fa3UyxMAyfC9Cvub+ztrrVPAOtYSOuWhL/74A3mlNwDdXvYyN2OOUdqotC/cyB+9pMw02fhc7UtID/z6y04Z3Kl8nvbKLHGcRVOyq8lwvwwcIHeKeb+YBljeGiuJnxulo/ZwuU2+q4huilWIcrhnNpwvVnSCulraoyfjbW867xdxYSqCuuGd8s7m8ObWq6Lvw0PGrDWbFt+hdJZIwWetZOuIvVF7jws2xybA0SX8EqrrUQbjn5IT7LfrnjjW5zY7+wKYmWC5+IdF38fxClwXDXgjx1xiMbfIab9n9HLmQWdX1os0HDXri2da7kWKdc3urdLzmWKVZ2iN/mjiSRD3Br37tNfF9VhXCbgTqxBD7z/QrcdaPQNSu+v6uyck7zGP9uG7+XftS3gv4f7uf7Ku4behK5hQ7ieal1NXs3w/E+Zrl0f/z/DySrHsZ7v0H91eS2jvf+Ny7YHoxrZv+UPkN732qegZo9eBICAzdhPohrUY/FZz2fnpx7ON5oF5njskrbDffd2oW6K0wsh3c431ImZA0t5P8zK2y40LNL7p1lIcSy5RCLNNXZBIJs5uwfcS3RbnjnlGlCFqYFNEStZcMD7b8a62CT4puWuW6zaKW01SvXrD3ZBXgx2X80HkT+hBa+/94kk9dwt43r8YH75TPh+WeP3+lU3GTdr8x5u8Vv+te4YJqFtkqFhQ2AM8qkb1Sbi/sKf4W7f62ORyCZh7q+7kPwBQaOpq4GN2u3uuH9yx9w95wrcWtPH3zy7f25exo+yF4p2bcarmGdCJxUoRwH4tETnsA1dvlV5Azv914n0WK3li35DrbHlQx3Vjj3GNzl6Tp8UNMrd/xXuCm9a27/EfgE0TPwVcfOi9/YPZSUGpVcya7ANdW3UmEghcsY3ShZtxYHjk2Od8VdHSbGZ92DCq5U7XGreQZqveHBoOfHhdEX8ZHNgdQdmV0J/KVM+uyDuhwfMd2HayCuJHGspkKYkuScmeL/01Y2XJswDR91LkhdLUId7WhB2uepu4LY5rg7xl/wEfTLJFqbWXnDtXC/beZryhLQsu/s78SlgPFB8v2xXXoL+FUL3XMU7id5dcGx9YGLmvFemfDWnVwooLh/b0oTVPK+0QPxQdHPuF/oy/hg7Gnc9H8cLtAtU0nQyO6f/F+xzY0Cx+9xt5rTcaHozdiH3BH/v48Kgd2Tay1OaQnucfE51sydsxIuNGca9YVwYflyXPM2GTitzPVvw12A9sctendRV4gfFdvBeu+6tW249vmr2A+Us55lcVnfwQXPxZNyexw4Ind+fzx6z2LJvs64i8ZNeB9eVlMdz58zvocncZeOX1OFMiCpN08Bv0n2z4tbot7CJ7gtgYL+e9nUOgM1eeiS6Xd/ErMHPiIekDt3hfiR9I+/0489a2xHxIrXGQ8f8yvcJPAIZXxbGshfi/j/tKWNUly7S2LjfxkubOb9tIq03L/ETYj7U1rZ5X7g+Ph/59g5HNJS+W8PG7IEtGTZroGbr2/GB8/Zsoo30ELhunDB6Dx89Z/Lyc2ObqF7bhfrygkkMT5xU+nxJObw5FgnXHh5HPdBXRifUHQ4Ltw+jQuxxzYiH1W3uTFvl+E+lJ/hGs5tKAj1RMn8vCGuKb6JuqHcVsVjpxbFRO2JC5g74ALyOBI3JVyrfAm52Mix7nyW/F4Ut3AslTtv0cb2TbXaYnkfX8V5q+MuDi/ig459KHBtiOV5ffy/M3X79YG4dvN3uTSpoiRVZC2A9/cP4pMci0KopfFWd8NlhuNxwfq2XN1fD9eEv0kzWsHa8lbzDMz0B65r8v+MOGuTgpVzYgPxL+Cg+Luc9u7/iDM1k33zxQ/lX2nDVGUeW9z/p7VvsQHO/I4Wi438w7gmYy0qzDrGXQMOpLRS0kUk0RriOffTiCVttVV8V7IEzHgZFq1X/uvYsc0f962BD5p7FKVppnz0wQcgl+OC4DE0U3iwpLP+BT6gN9ya9TvcjHpTFCBmw+NZvlvhWt1wDdpHmWCA+yI+FY/1pBHm06a0uTHvD+GDh9/gWjoreJf9iYIzHp/zPVy4HVmpPsTtIFxj/Awu2MyenHMCcFNB2ueytjP+HokLRBU1hK15i++nbyPO3xWPbjCN4pX0tsAHNH2z8o5/MyXWhcBRZervpvH4A7hf9ci4fxNcu7pl7po9k2u8TmlicDfcNfDSWI/Po67AXDg5rj1uNc/ATH/gUmX7E/ATPnkgH9Ijq2DrEn0Z0/25cxeNle9rfESeLhjQlehHqa1R72go7juU9zVaPzb0d+Mml0LTPd7J98HDjvwlNii3Zh8+PuquuGygtia9t3ZvCWiGMlwJDyV2CLBAsn9FXCjKFq1otnBUscNcARfUsoVM5sInC91LlSHNGrhX1qZmIc02o+5qf0vjPoM34wqEN4lrrueuczeluL2dcU3b5bjp910St4ii9roF3pfhg4lJ5CITJOW7AUmor/isd+IThf6U738K7jGEXHi3+LxfAUsUnH8MJZ/NpXEhd/94rN2YkmO9LvSTx/v9N6i7tGqn5J1dSuLfTN1oBB/jvsUn4r7GN2Z9UdF3GfugHrjW/0w80kIqkPbBBef9snpd67JrbVvNMzBTH7bUWM6P+6dcEz/of+ArSBXN1qy4Lm88tgTuU/Zv3O9oNSoECNbW4HvaHzg5/t8x/17w0fKLwIG5/b1xR/Tn8YHDHXiHfwiuVb0Bn+jwJa1wAkFb35AloKnllq79/kpsl/6Oa4MWj8eGl+t0Z+C+Wdt2Ba59egfXFP2WaMbGtXEVF9No5L2yxU16J3WmM3EmOi7ILk/OTB2PDcAHnCcn11su5nsacWnZSm11C77DwoUpcLPws8AlBce2JVlUoeD49FUPc/s3wbXPhfMm4jnD8IlfH8W2cF7akZBaxfvqjM8j+ZG4jG1ybBl8Gdt6UUti/Tsi+T0HLj9MpBTzNY2V+gdgfPz/P7Ge7p2krydfFL3z9r7VPAM1eWhXzx8d/18R97F7FdfSzdtQRclVqjRe22axIb4dF4gU5Lzx76ZjfA9vZ+8oK/NcWc9BzhSEj4KvwzUzy8QG5Clc47IiPmq9D7il1s+pTVt+I4mZjA/GXoj/r562Jc0piOFuNR9RijmaTfA5GzdxdmvGe60AfJTbdxzuo/pmFBwqLumJW7k+TYU0XPHwBGVm+Nf4nS6Ba09/iM/aHPFu18ZXUOxSxbnL4W5Oz8Q0w5uz/rS1Dddurpj8PjEKme/i8VYfwn1Nj4zHp7sKxr+nkKzylZ0T+/wl8mmo6164FO669g0+aWp4rcujrWw1z8BMf2BXwdcL/ouPbl+KFXVbcvHOkvMy00DveN7fqDvC6oQLWvdSJhyVtqre04axQ7qcujEE0/W70wHDmrFRyC89ODI20s/Fdz+MNjKBQFv72XDz7n3EgXKs+2vFY2eRm9jRjPedo6DjXQ+fHHI/iftBM9xrYyqHNHsW2KKK6yyHuw9kYes648tcTqPCylAz+X3WiXOaPN/LVF5hK9PEzUMFhQmNFDYphal7mnY6QQf3jb4aHxidmZUDrtQ4CZ+sdhLwyyTNnCQWPdwd5jM8fFz2rvrgq6Jl4adS98IfcTeDzILQEXfvuQ4XjhUZpZp3V+sM1PThXaisE+8O14R+T5kl1yiNlq6JH/3puPbvXWCn5LwWW2JwVt0ojVqzBmAlfEbkHyk5vZeb0PYy0YSDd/SdknfVA5/A8OtaP6M2bSG4FrBg34X44PckSqscDQb+R1zytzk6teQ7WyF2ppPw5Z9Tn9FuNPNkDmYwpFn2TeOC3xGx3U1jVh9AMnu6Ru81FVBnxy05C8Tfc+BuSONxQbt73D99Oc74d13gsWru0ci8NXuYura24QOAHXB3l0fxqBOV3PrOiudtmuzbN/ZLt8dv9g5KA7DsXebdC8/FNarZt9cHn4T9CrByrcultW81z0Br2agrsA4pEoiSSrgSyWQc4LFYab/HQ5aMrPXztLUN11B/gAul2xDXP8Y1pW9Q2R/rN7g2ZWmSGZbZO8MF1ZuRX6q2VrBFgeErYN7c/i0oraa0RKzPtwB/jcebzccwfhff4xahx3A/z6NxrVODJuUZuG+jQ5rFcpju05rsPzLmPdM813wSCqVB9r74nIU7iDO6436Lz7N9kmYQbumZFsvlHUqrjrXZmfqtecOXOv021rk7KVBi4NrPNfGB1UO4a8rIeGxVfGB0E+4z3C3uz2SEcu6Fh5KY/IHZal0WbWGreQZa00ZdJ+hKo6zTgbPj/ztTcpa+CNcSbFDrZ2lrG25++QDXkuwDXIxPmNoUD1/zcxQ26/n9xgblD7hA+2fc7y4fW/AhYKtaP6c2bSG4RiX+XQSfONU3/p4f9xF9K3aCf6RgwsUM3DfrSFcELkz2b4JPQnwifm+FSz434/NXFdIMn2D2bGwbbsL9CI+I3/jaeBi785qjbJrhmdKV876kNBP8LuJiCRS4UgDv49FjuuA+9VPxeJzZu8r6pGH5dk1bVe+lS1KGWZlehUeMWAD3O30cdwsomlA9GLc4XIwPjI4vEjCpa8Gr5F74b1yTKyG12ndY6wy0hQ2fST4g+T2E6MeC+6JuHf8/Hti21vltq1vsPJ+Inc+Q2DhcHxuSF3GNw0IV0vfDJ1S9iGs0siDpWwATav182rSFUE8rODfwIR7yZq9k/3AK1hdvpvsPxQdul0YhMTVXH4W70cwU7SQNhDSL+RuJa8DOwZUEz+AhAW+NbcK01tTu4lETMteNX+I+jT3js/yDumHHNgHeiv93waMv7BqfcRKwezw2Bz54aZaYtu1pw2Nw70QMHQksG99JJrwOjn3E9rl0+Xi4ffDQcf/A46PvXMW9K7kX7ljrsmkrWzYCEGUws+VwTdwf4u+hIYSJ8X/DTWddcefoG/AZhS/WKr9tHTMbStSOhhDOjfsWxjujhUMIN1dxjYVx7fa3eOf2D9ykeENL5VuIajGzjiGEqWa2FT4A/ruZ7YX7i76Dx1N8oAXvPwLXHi0FHB5CODV3vGsI4ceWun/uXp3wlaXmDiHcVc35IYSfzWxBXHM1CDfH/qmFs1o1ZrY0HoFgDTN7HLg9hHCCmW0LHBxCGJmcuwTu53gsHjVmWghhVzPriE/YOR53B5kIvBZC2GUmP06bx8zOwBfPeBb3jb4QGBtCODP7FuN5FkIIZjYv7rLxHK7pfhxfwOcZXIHSDw+hNhAPo/ZClflI7zUE+Dj7LSojQbUKzKxnCOFbM9sRnxF7PfCvEMLXZvYbPO4nuAP8oTXLaBvEzDbENanXAD+EEN4ys6XwCSWPhhBOaOJ1DdfIXg08FUJYo7nyLERzYGb/wX2vr0j2/Q2fwX4nvg74Ty14/21xDeUP+ETEu1vqXu0JM5sbV1zMhmvT5jez/rhZ/4gQwrW583fC3T8MGB1CuDc51hUXWL/HY7Gqw24CUQFyKh7jvBeupX+/SFA0swPjuc/j77Ej8Ak+e/9dYBRuBQkhhIMbmY8sas3UTDBu6jO1JySoVsDMdseFz5fi7w3x9eb74P6Qt4cQHjWz4fjkiG9CCNNqld+2iJn9Fp8JOxU32U/FZ0iCj3z/ha/x/HETr98Zn808qRmyK8QMYWYdQgjTzGxVfCB1RQjhJTPrHEKYEs8ZgU80OrmF8tAD19z9EAWhY3ErxvP48o4zRZvaHLSGzj55p9PzYmZ98BnjY/BB+EDcSrRPmWs8A/TFNXdjgUNDCB/OhOy3K6KF9BS8rC/H5z28n69DZjYa12ZPxH1Zby03aEw1paJlkKBaBjPrgDvBr42HTDk+hDDJzHoCW+HLoXXEQ1f8K3Y2NW802xpm1guPNbcaPtN5MO6j9RTuR7QVPnvyxJplUohmJJp1XwIWwido7BpNjtlKRD8n53aY0cFvYi7fEPfV6437od6TaVGjcLxp3g1AVCYxFxs+o3st3Gx8aQjhf2a2KD7x60ng0xDC97n0mRvIL0IIb5vZIrjv8CK4oPSXEMJ3M/OZ2gNmtituCf0On/z8WcE5Rpx3grsN/AV4TkqPmY8E1QYws+XxkVcP3Gxzedw/DyWB9bEQwlE1y2QbxMyyBRP2wLUIj+ErgtwRQnjRzHYGRuCTKM4MIdxYq7wK0RJEv9TT8ZB2+4UQnoz7m23AmwhS3fHZ5afi1p9dgIAPtK8MITzXHPdrbyTlewE+0L4d2Br3j/8rrjGv4wNZ8P/gmPbDEMLLcd/m+PwH8AmkX87M52oPxG9i6xDCxQ2cNwz3bV0WuBv4ewjhtZmQRRGRoFqGnCluHdyH6Bd4p3JACOH+eGxlPKbq+Jpltg1iZpfiwv8LuOZ6F3zFmc/wTvuNaLbvEkL4tmYZFaIZSDWjZtYj05JF0/t5uHBzC7BPCOHTFrj/OXhot99Ewegt4AR8xv3HuKn5X8193/ZA8g6PDSFMiPsOwy1DbwPHhRAeyaXJNKn74AqPr/BQXNfh8Z5/jOdtpkmgrQMzWw2fmHukFCczFwmqZUj8jo7Fzf+H4g7VB+EN0EXAnySgNh4zWxP321o87ZSTGbD9gDVDCB/UJodCtAxmdjBuCl4WuBH4cwjh82givgUfBN/SzPfsBhwGPBBCeNDM7gKeDiEcaWaX4KHbjmvOe7Ynoj/qMcB/0olSZjYbHhrpphDClcn+TAvbH4/ysFGc63At8FMIYXszWzKE8PzMfRIhWicSVCsQfcbuwUON3Jns3wVvgH7E15b/ukZZbJOY2cv4ajvnZn55wNTYePfA/ef2DSGMq2lGhWgGEu3ZTniw+v9v7+5jta7LOI6/LyGjB4lkIisQqmGtBx/QSoECnYhgD4haWriloS5XOUKxGM7hTDaNhmiZsikZupxgDTIjBUOcZAK6OdNJKWgLRqBGZAXmpz+u7w2/zng4cM7hPuf8Pq9/HOc+576/x3u7z/X7fa/v57qczMxcSJ4gfqoje9sj45LeIu/a/Ys8QHKhpOcjYilwo1oRDWW7VN7T08lczCPI3t+Jjd22VjzHBHJU9/jyHi0ht/m3RMTPgNmSVnfU72AHxmdRDr5Dmr2Azqxs1f0Z+Ga5Om74OdlkfZqL1P0TGef1UeAPkbFfb0l6s3KYBPL0f989P4tZ11E5Efx14BuSHiBD91dKWgNMjIjzYefFcZs1nicivkxORXq6bEu/CmwGroqIm8lxji5S91PlPZ1JGfpCHoJ6MCKWROZk7stzwMCIOAK4iex93BIR44ETXaR2Ti5SDz4XqvvWyLebFBGfLA3YJwCXkAeAbP8sAq4g802vi4iPR8ShkBcGpXfvvWS2o1mXVvqsG/99GTgyIvqQW8WXlW8bQ168NS6O26zyPCPIscKNIP8d5GfaMeRd1rPb4/XqpHIRMBJ4QtIMSWskTSPbOrYCL5c76HvzCvAseQBrIPl52IeMT+o0AwzMms1b/3sREYeXiJExwBRgG7lVtxGYK2luUxfYhZUP5NnAULLfd7GkFyPiS8ANkgY3b3VmB67S3z6aHJG6VNIrkbnMxwODyak0F0bEccAyctzv6+2xrVjpgRwNzCDHjV6sFlmPjdiqtrxWXZWIsbvIXO0rJc1p8fho4BlJG/fxPH3JwvQsss2sf/m5yztk4WZdkAvVikre4EnABeS4tN7AdPLDfigZ6bJB0rPNW2n3ER53at1Q5GjQP5EZzHeXLd3+ZG/7scCN5OjQD5OF7MxoQ3D47grciJhIzjAfTE7TW+wDOu2jvL+fJQvVU8iYr1ktY4sqFy2Hkn34O6OqKBOKyr8/QV7UPAH8Ux04kcysq3GhuhsR8SJwD7lN/QOgp6RhEdFLkrek21n50Pa4U+vyKoXJrUA/SWdXHhtBBsKfR45I3UyOCV5eHm+Pu6kjgW2N/sbS/3gBmUe8FVgBPCRpfVtep44q720foH85jPZuMhVmApkHvRyYKWlbi5+dS7aKLZa0ufL1nuTf4R2RIfSPtSx2zerOhWpR2S47j5x7PSpyCtVa4IvAKjKa6j5JLzVzrd1VeNypdQOlkFkOfFW7AtxvIftB/0imhaytbu+2pUitFFBTgWHAc5K+Vx47VNL2iDiWPPBzInmIywNKDlBE3EDOfZ8HLJO0MTIUfgw5+esaSQ+V7238XZlF/h1ZQ/YIr6oWs+Ui5hGgrw/omv2/WheqlYiRarj/GcB3JJ0eEXeRhdM55YN+HjDKhZSZ7Um54LofuFfS/IiYDFxN3k1dRxYzZwDnAlvbche1UggNAFaTo4jXls+1KeV1rpf0SPn+CWRu6poD/w3rLSIGAd8CTiJH4f6CvDDZQfYa7zZbOyIGkv2oJ5efmQe8IOk/EfEU8JvGBYaZ7VLrQrUhIn4H3CnppxHRj8wZXEKO9xxU7kgsBVZLmtq8lZpZVxA5cehaYDvwNLCgcfgyIj5IhvufKulv7fR6C4HNki4t/+5LhsmvBIaQ285XqAOmXtVVRAwHvk0mKCwCfilpZSt+bhQZb/gesrWsP/ne9OuwxZp1YbUvVMsdhgXln08Ck4F+5MGe3mSEzDjgQ5JObsoizazLiZw2NQJYJGlD5esLgb9Luigqo1Xb8DqHk/3d0yU9Wb52GfABSVdGxGeAn5AjPu9ry2vVUWPHLSLGAY9Ler3F4/PIU/tTJd22H897Mfn35iPAOfJYTrPdqn2hCjvnMg8AegFfA+4kP9jHkicxHyXH43lcqpkdkLI9Px6YCgwpW77tUaj2ICOulki6vtIOUG1pmkseVLy9jb9GLZX/x78lT/hfLen7lceGke/rtS0PUbXieXuRI1QX7PObzWqq1oH/5cQl5B3V3sDD5Ci8I8meo9eAaZLucZFqZm10FBkIP7kUqT3aWqTCzilJjwOnlYg9lSSNNwEi4hjg88C9e3ka242ImB0Rn5L0X0mNxIZLI+IvEXFuaeOYQ/Ya71eRCiDp3y5SzfaudndU93QHIyI+TQbQz5N0W9nmuRXoAwyW9NpBXaiZdSuRE43eeSAFTSueewB5of02MoD+/pLdOYiM2vu1pGva+3W7s4gYAlxH9pD+HpgtaUO5CJhOpsCsBTZJGtu8lZp1b7UrVBsiYgY5a/kwclbzeuBM8tTsvEagf0ScKmlZ0xZqZtYKJSLpKuArwD/IhIHDyEOgk5q4tC6rFKvDgC8Afcndtx81khrKwJL1ytHPZtYBalmoRsRZwEIyG3UdcAIZwH0c+aG0Ahjvu6hm1pVExNvJ4Pmx5Nb/Y8DzLqRar2WmbYkbG0SG+g8HegK3SHqwSUs0q5W6Fqo9gEnANPLg1ApyNOqrZO7gKkkPN2+FZmbWDOUu6lFk29dm4A2gMZFwJDn69nNkxNiUxoE1M+sYtSxUG8oEmZuBo4H5wHzfRTUzq6+IeAb4GHmgdgg5SGE42ad6PNlWcTTZInZRs9ZpVhe1LlQbSp/RHeRowznAo9V5zGZmVg8R8V3gEnJQw4+BF4BNwPuALWTx+ldywML2Ji3TrDZcqBblJOeZwN3ADEk/bPKSzMysCSLi/cAsYCjwAJmr/VK1MG3Zy2pmHcOFagsl0uVdbgEwM6u3iDiFHIXbB7gd+BWwzgWq2cHjQtXMzGwvKuNO3wDGSdrU5CWZ1YYLVTMzs32IiHcA50u6o9lrMasTF6pmZmZm1ikd0uwFmJmZmZntjgtVMzMzM+uUXKiamZmZWafkQtXMzMzMOiUXqmZmZmbWKblQNTMzM7NOyYWqmZmZmXVK/wNc9BPUsUPsNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlim = (0, TIMEOUT + 1000)\n",
    "ylim = (-1, len(df_uni.algorithm.unique()))\n",
    "#df_uni.loc[:, \"test_plot_time\"] = df_uni.normalized_execute_time + df_uni.normalized_train_time\n",
    "plotted_values = [\"normalized_overall_time\", \"normalized_train_time\"]\n",
    "sort_by = \"normalized_overall_time\"\n",
    "groupby = \"algo_display_name\"\n",
    "\n",
    "yticks = [1e-9, 1e-6, 1e-3, 1e0][1:]\n",
    "ylabels = [\"1 ns\", \"1 μs\", \"1 ms\", \"1 s\"][1:]\n",
    "y_range = 1e6*30\n",
    "def get_absolute_from_ns(ns):\n",
    "    return np.log10(ns * 1e6) / np.log10(y_range)\n",
    "\n",
    "training_types = df_uni.algo_training_type.unique().tolist()\n",
    "sorted_algos_unsupervised = df_uni[df_uni.algo_training_type == \"UNSUPERVISED\"].groupby(groupby).mean()[sort_by].reset_index().sort_values(by=sort_by)[groupby]\n",
    "sorted_algos_supervised = df_uni[df_uni.algo_training_type != \"UNSUPERVISED\"].groupby(groupby).mean()[sort_by].reset_index().sort_values(by=sort_by)[groupby]\n",
    "algo_groups = [(\"(Semi-)Supervised\", sorted_algos_supervised), (\"Unsupervised\", sorted_algos_unsupervised)]\n",
    "\n",
    "# create figure\n",
    "fig, axs = plt.subplots(len(algo_groups), 1, figsize=(single_column_figwidth, 12))\n",
    "\n",
    "for a, (title, algos) in enumerate(algo_groups):\n",
    "    ax = axs[a]\n",
    "    df_tmp = df_uni[df_uni[\"algo_display_name\"].isin(algos)]\n",
    "    for i, plot_val in enumerate(plotted_values):\n",
    "        if i == 0:\n",
    "            auroc_train = df_tmp.groupby(groupby).mean()[plot_val].loc[algos].reset_index()\n",
    "            cs = [colormap(training_types.index(df_tmp[df_tmp[groupby] == j].algo_training_type.tolist()[0]), False) for j in auroc_train[groupby]]\n",
    "            ax.bar(auroc_train[groupby], auroc_train[plot_val], align=\"center\", color=cs)\n",
    "            continue\n",
    "        if a == 0:\n",
    "            axh = ax.twinx()\n",
    "            axh.set_ylim(0, 1)\n",
    "            axh.tick_params(axis=\"y\", which='both', length=0)\n",
    "            axh.set_yticks([])\n",
    "            auroc_train = df_tmp.groupby(groupby).mean().loc[algos].reset_index()\n",
    "            x_range = range(len(algos))\n",
    "            overall = auroc_train[\"normalized_overall_time\"]\n",
    "            rel_to_overall = auroc_train[plot_val] / overall\n",
    "            hlines_y = overall.apply(lambda x: get_absolute_from_ns(x)) * rel_to_overall\n",
    "            axh.hlines(hlines_y, [x-0.4 for x in x_range], [x+0.4 for x in x_range], color=\"black\", linestyle=\"dotted\")\n",
    "        \n",
    "    ax.set_yscale(\"log\")\n",
    "    ax.set_ylim(yticks[0], yticks[-1])\n",
    "    ax.set_yticks(yticks)\n",
    "    ax.set_yticklabels(ylabels)\n",
    "    ax.set_xticks([i + 0.5 for i in range(len(algos))])\n",
    "    ax.set_xticklabels(algos, rotation=60, ha=\"right\")\n",
    "    ax.set_title(title)\n",
    "    ax.tick_params(axis='x', which='both', length=0)\n",
    "    ax.set_ylabel(\"Avg. runtime per data point\")\n",
    "\n",
    "\n",
    "dagger = ax.text(x=0.001, y=0.001, s='†', color='black',size=0)\n",
    "\n",
    "class TextHandler(HandlerBase):\n",
    "    def create_artists(self, legend, orig_handle,xdescent, ydescent,\n",
    "                        width, height, fontsize,trans):\n",
    "        h = copy.copy(orig_handle)\n",
    "        h.set_position((width/2.,height/2.))\n",
    "        h.set_transform(trans)\n",
    "        h.set_ha(\"center\");h.set_va(\"center\")\n",
    "        fp = orig_handle.get_font_properties().copy()\n",
    "        fp.set_size(fontsize)\n",
    "        # uncomment the following line, \n",
    "        # if legend symbol should have the same size as in the plot\n",
    "        h.set_font_properties(fp)\n",
    "        return [h]\n",
    "\n",
    "handlermap = {type(dagger) : TextHandler()}\n",
    "\n",
    "fig.add_subplot(111, frameon=False)\n",
    "upper_legend = [\n",
    "    Line2D([0], [0], color=\"w\", marker=\"o\", markerfacecolor=colormap(i, False), markeredgecolor=colormap(i, False))\n",
    "    for i, _ in enumerate(training_types[1:], 1)\n",
    "] + [dagger] + [\n",
    "    Line2D([0], [0], color=\"black\", linestyle=\"dotted\")\n",
    "]\n",
    "legend_labels = [\"Semi-Supervised\", \"Supervised\"] + [\"includes timeouts\", \"relative train/test split (linear scale)\"]\n",
    "axs[0].legend(\n",
    "    handles=upper_legend,\n",
    "    labels=legend_labels,\n",
    "    ncol=1,\n",
    "    handler_map=handlermap,\n",
    "    loc=\"upper left\",\n",
    ")\n",
    "axs[0].set_ylim(yticks[0], yticks[-1]*30)\n",
    "\n",
    "lower_legend = [\n",
    "    Line2D([0], [0], color=\"w\", marker=\"o\", markerfacecolor=colormap(i, False), markeredgecolor=colormap(i, False))\n",
    "    for i, _ in enumerate(training_types[:1])\n",
    "] + [dagger]\n",
    "legend_labels = [\"Unsupervised\"] + [\"includes timeouts\"]\n",
    "axs[1].legend(\n",
    "    handles=lower_legend,\n",
    "    labels=legend_labels,\n",
    "    ncol=1,\n",
    "    handler_map=handlermap,\n",
    "    loc=\"upper left\",\n",
    ")\n",
    "\n",
    "#plt.xlim(10e-10, 10e1)\n",
    "#plt.ylim(*ylim)\n",
    "\n",
    "plt.tick_params(labelcolor='none', which='both', top=False, bottom=False, left=False, right=False)\n",
    "\"\"\"plt.tick_params(\n",
    "    axis='both',          # changes apply to the x-axis\n",
    "    which='both',      # both major and minor ticks are affected\n",
    "    bottom=False,      # ticks along the bottom edge are off\n",
    "    top=False,         # ticks along the top edge are off\n",
    "    labelbottom=False,\n",
    "    left=False,\n",
    "    right=False,\n",
    "    labelleft=False)\"\"\"\n",
    "plt.subplots_adjust(hspace=0.5)\n",
    "plt.savefig(\"plots/runtime-overall.pdf\", bbox_inches=\"tight\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_scores_df(algorithm_name, dataset_id, repetition=1):\n",
    "    series = df.loc[(df[\"algorithm\"] == algorithm_name) & (df[\"collection\"] == dataset_id[0]) & (df[\"dataset\"] == dataset_id[1])]\n",
    "    params_id = series[\"hyper_params_id\"].item()\n",
    "    dataset_learning_type = series[\"dataset_training_type\"].item().lower().replace(\"_\", \"-\")\n",
    "    \n",
    "    path = (\n",
    "        (gutentag_result_path if dataset_id[0] == \"GutenTAG\" else benchmark_result_path) /\n",
    "        algorithm_name /\n",
    "        params_id /\n",
    "        dataset_id[0] /\n",
    "        (f\"{dataset_id[1]}.{dataset_learning_type}\" if dataset_id[0] == \"GutenTAG\" else dataset_id[1]) /\n",
    "        str(repetition) /\n",
    "        \"anomaly_scores.ts\"\n",
    "    )\n",
    "    return pd.read_csv(path, header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection_name = \"GutenTAG\"\n",
    "dataset_name = \"sinus-diff-count-5\"\n",
    "dataset_id = (collection_name, f\"{dataset_name}.unsupervised\")\n",
    "algorithms = [\n",
    "    \"GrammarViz\",\n",
    "    \"Subsequence LOF\",\n",
    "    \"DWT-MLEAD\",\n",
    "    \"DeepAnT\",\n",
    "    \"Donut\",\n",
    "    \"Subsequence IF\"\n",
    "]\n",
    "anomaly_highlight_color = \"red\"\n",
    "anomaly_highlight_color_alpha = 0.4\n",
    "colors = cycle([\"orange\", \"purple\", \"brown\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load dataset details and AUROC scores\n",
    "if collection_name == \"GutenTAG\":\n",
    "    dmgr = Datasets(gutentag_data_path, create_if_missing=False)\n",
    "else:\n",
    "    dmgr = Datasets(benchmark_data_path, create_if_missing=False)\n",
    "df_dataset = dmgr.get_dataset_df(dataset_id)\n",
    "data = df_dataset.iloc[:, 1]\n",
    "\n",
    "# find anomalies\n",
    "s_anomaly_labels = df_dataset[\"is_anomaly\"].diff()\n",
    "anomalies = []\n",
    "for begin, end in zip(s_anomaly_labels[s_anomaly_labels == 1].index, s_anomaly_labels[s_anomaly_labels == -1].index):\n",
    "    anomalies.append((begin, end))\n",
    "\n",
    "auroc = {}\n",
    "df_scores = pd.DataFrame(index=df_dataset.index)\n",
    "skip_algos = []\n",
    "algos = []\n",
    "for algo in algorithms:\n",
    "    algos.append(algo)\n",
    "    # get algorithm metric results\n",
    "    try:\n",
    "        auroc[algo] = df.loc[(df[\"algorithm\"] == algo) & (df[\"dataset\"] == dataset_name), \"ROC_AUC\"].item()\n",
    "    except ValueError:\n",
    "        warnings.warn(f\"No ROC_AUC score found! Probably {algo} was not executed on {dataset_name}.\")\n",
    "        auroc[algo] = -1\n",
    "        skip_algos.append(algo)\n",
    "        continue\n",
    "\n",
    "    # load scores\n",
    "    try:\n",
    "        df_scores[algo] = load_scores_df(algo, (collection_name, dataset_name)).iloc[:, 0]\n",
    "    except (ValueError, FileNotFoundError):\n",
    "        warnings.warn(f\"No anomaly scores found! Probably {algo} was not executed on {dataset_name}.\")\n",
    "        df_scores[algo] = np.nan\n",
    "        skip_algos.append(algo)\n",
    "selected_algorithms = [a for a in algos if a not in skip_algos]\n",
    "\n",
    "# Create plot\n",
    "fig, axs = plt.subplots(2, 1, sharex=True, figsize=(single_column_figwidth, 3), gridspec_kw={\"height_ratios\": [1, 2]})\n",
    "axs[0].plot(df_dataset.index, data, label=\"timeseries\")\n",
    "# axs[1].plot(df_dataset.index, df_dataset[\"is_anomaly\"], label=\"label\")\n",
    "\n",
    "# mark anomalies:\n",
    "anomaly_kinds = [\"pattern-shift\", \"frequency\", \"pattern\", \"variance\", \"extremum\"]\n",
    "minimum = data.min()\n",
    "maximum = data.max()\n",
    "for (begin, end), kind in zip(anomalies, anomaly_kinds):\n",
    "    width = end-begin\n",
    "    if width < 2:\n",
    "        width += 2\n",
    "        begin -= 1\n",
    "    axs[0].add_patch(Rectangle(\n",
    "        (begin, minimum),\n",
    "        width,\n",
    "        maximum-minimum,\n",
    "        color=anomaly_highlight_color, alpha=anomaly_highlight_color_alpha\n",
    "    ))\n",
    "    pos = (begin+end-begin, maximum)\n",
    "    axs[0].annotate(kind, pos, ha=\"center\", xytext=(0, 5), textcoords=\"offset points\")\n",
    "\n",
    "method_families = sorted(set([algo_meta[algo][\"method_family\"] for algo in algo_meta]))\n",
    "for algo in selected_algorithms:\n",
    "    algo_metadata = algo_meta[algo]\n",
    "    name = algo_metadata[\"display_name\"]\n",
    "    color = method_family_colormap(method_families.index(algo_metadata[\"method_family\"]))\n",
    "    axs[1].plot(df_scores.index, df_scores[algo], color=color, label=f\"{name} ({auroc[algo]:.4f})\")\n",
    "\n",
    "# display styling\n",
    "axs[0].axis(\"off\")\n",
    "axs[0].set_xlim(2000, 8500)\n",
    "\n",
    "spines = axs[1].spines\n",
    "spines[\"top\"].set_visible(False)\n",
    "spines[\"right\"].set_visible(False)\n",
    "\n",
    "# create legend\n",
    "lines, labels = axs[1].get_legend_handles_labels()\n",
    "# lines.append(Rectangle((0,0), 1, 1, color=\"yellow\", alpha=0.75))\n",
    "# labels.append(\"Anomalies\")\n",
    "li2, la2 = axs[0].get_legend_handles_labels()\n",
    "lines = lines[:2] + li2 + lines[2:]\n",
    "labels = labels[:2] + la2 + labels[2:]\n",
    "legend = fig.legend(\n",
    "    lines, labels,\n",
    "    loc=\"center\",\n",
    "    ncol=3,\n",
    "    bbox_to_anchor=(0.5, 1.15),\n",
    "    borderaxespad=0.,\n",
    ")\n",
    "\n",
    "plt.savefig(\"plots/example-scores.pdf\", bbox_inches=\"tight\", bbox_extra_artists=[legend])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
